无人机视角下的目标检测研究进展

冷佳旭; 莫梦竟成; 周应华; 叶永明; 高陈强; 高新波

doi:10.11834/jig.220836

复杂场景图像目标智能检测 | 浏览量 : 0 下载量: 14 CSCD: 0

PDF
导出
分享
收藏
专辑

无人机视角下的目标检测研究进展
Recent advances in drone-view object detection
2023年28卷第9期页码：2563-2586
纸质出版日期： 2023-09-16 ，
DOI： 10.11834/jig.220836
稿件说明：

移动端阅览

冷佳旭，莫梦竟成，周应华，叶永明，高陈强，高新波. 2023. 无人机视角下的目标检测研究进展. 中国图象图形学报， 28(09):2563-2586

Leng Jiaxu， Mo Mengjingcheng， Zhou Yinghua， Ye Yongming， Gao Chenqiang， Gao Xinbo. 2023. Recent advances in drone-view object detection. Journal of Image and Graphics， 28(09):2563-2586
冷佳旭，莫梦竟成，周应华，叶永明，高陈强，高新波. 2023. 无人机视角下的目标检测研究进展. 中国图象图形学报， 28(09):2563-2586 DOI： 10.11834/jig.220836.

Leng Jiaxu， Mo Mengjingcheng， Zhou Yinghua， Ye Yongming， Gao Chenqiang， Gao Xinbo. 2023. Recent advances in drone-view object detection. Journal of Image and Graphics， 28(09):2563-2586 DOI： 10.11834/jig.220836.

摘要

在人工智能技术的支持下，无人机初步获得智能感知能力，在实际应用中展现出高效灵活的数据收集能力。无人机视角下的目标检测作为关键核心技术，在诸多领域中发挥着不可替代的作用，具有重要的研究意义。为了进一步展现无人机视角下的目标检测研究进展，本文对无人机视角下的目标检测算法进行了全面总结，并对已有算法进行了归类、分析和比较。1）介绍无人机视角下的目标检测概念，并总结无人机视角下目标检测所面临的目标尺度、空间分布、样本数量、类别语义以及优化目标等5大不均衡挑战。在介绍现有研究方法的基础上，特别整理并介绍了无人机视角下目标检测算法在交通监控、电力巡检、作物分析和灾害救援等实际场景中的应用。2）重点阐述从数据增强策略、多尺度特征融合、区域聚焦策略、多任务学习以及模型轻量化等方面提升无人机视角下目标检测性能的方法，总结这些方法的优缺点并分析了其与现存挑战之间的关联性。3）全面介绍基于无人机视角的目标检测数据集，并呈现已有算法在两个较常用公共数据集上的性能评估。4）对无人机视角下目标检测技术的未来发展方向进行了展望。

Abstract

Given the support of artificial intelligence technology， drones have initially acquired intelligent sensing capabilities and have demonstrated efficient and flexible data collection in practical applications. Drone-view object detection， which aims to locate specific objects in aerial images， plays an irreplaceable role in many fields and has important research significance. For example， drones with highly mobile and flexible deployment have remarkable advantages in accident handling， order management， traffic guidance， and flow detection， making them irreplaceable in traffic monitoring. As for disaster emergency rescue， drones with aerial vision and high mobility can achieve efficient search and safe rescue in large areas， locate people quickly and accurately in distress， and help rescuers control the situation， thereby ensuring the safety of people in distress. This study provides a comprehensive summary of the challenges in object detection based on the unmanned aerial vehicle （UAV） perspective to portray further the development of drone-view object detection. The existing algorithms and related datasets are also introduced. First， this study briefly introduces the concept of object detection in drone view and summarizes the five imbalance challenges in object detection in drone view， such as scale imbalance， spatial imbalance， class imbalance， semantic imbalance， and objective imbalance. This study analyzes and summarizes the challenges of drone-view object detection based on the aforementioned imbalances by using quantitative data analysis and visual qualitative analysis. 1） Object scale imbalance is the most focused challenge in current research. It comes from the unique aerial view of drones. The changes in the drone’s height and angle bring drastic changes to the object scale in the acquired images. The distance of the lens from the photographed object under the drone view is often far. This scenario results in numerous small objects in the image and makes capturing useful features for object detection difficult for the existing detectors. 2） Different regions of drone-view images have great differences， and most objects are concentrated in the minor area of images， i.e.， the spatial distribution of objects is enormously uneven. On the one hand， the clustering of dense objects in small areas generates occlusion. The detection model needs to devote considerable attention to this occlusion to distinguish different objects effectively. On the other hand， treating equally different areas wastes many computational resources in vanilla areas， limiting the improvement of object detection performance. 3） The problem of class imbalance in the drone view is divided into two categories. One is the positive-negative sample imbalance problem caused by the gap between the front and rear views shared in the image. The other is the imbalanced numbers of different categories caused by the number of samples in the real world. 4） The semantic pieces of information defined by different category labels in the drone-view object detection dataset are often similar， resulting in only subtle differences between different categories. However， significantly different representations of objects exist in the same category， which together form the semantic imbalance problem. 5） Drone-view object detection often faces the problem of unbalanced optimization targets， i.e.， the contradiction between the high computational demand for high-resolution images and the limited computing power of low-power chips is difficult to balance. These unbalanced problems bring enormous challenges to object detection from the UAV viewpoint. However， even the most advanced object detection algorithms currently available can hardly achieve an average accuracy rate of 40% on aerial images， which is far below the performance of general object detection tasks. Therefore， many scholars have conducted many studies. These research methods can be summarized as optimization ideas to solve these imbalance problems. In this study， we collect relevant research works， which are sorted and analyzed according to the countries of authors， institutions， published journals or conferences， years， the category of methods， and the solved problem. The present study presents the challenging problems solved by previous research and the development trends of existing methods. This study also focuses on the methods of improving drone-view object detection performance in terms of data augmentation， multiscale feature fusion， region searching strategies， multitask learning， and lightweight model. The advantages and disadvantages of these methods for different problems are systematically summarized and analyzed. Besides introducing existing methods， the present study compiles and introduces the applications of drone-view object detection in practical scenarios， such as traffic monitoring， power inspection， crop analysis， and disaster rescue. These applications further emphasize the significance of object detection in drone view. Then， this study collects and organizes UAV datasets suitable for object detection tasks. These datasets are present from various perspectives， such as year， published journals or conferences， annotation information， and number of citations. In particular， the present study provides the performance evaluation of the existing algorithms on two commonly used public datasets. The presentation of these performance data is expected to help researchers understand the current state of development of drone-view object detection and promote further development in this field. Finally， this study provides an outlook on the future direction of drone-view object detection by considering the aforementioned imbalance problems. The promising research includes the following： 1） data augmentation： providing the network with enough high-quality learning samples by considering the specific characteristics of drone-view images based on the conventional data augmentation strategy is a good idea； 2） multiscale representation： how to avoid the interference of background noise in feature fusion and effectively extract information at different scales using an efficient fusion strategy is an urgent problem to be solved； 3） visual inference： using information unique to the viewpoint of drones， mining contextual information from images to facilitate image recognition， and using easy-to-detect objects to improve the performance of difficult-to-detect objects are directions worthy of deep consideration.

关键词

计算机视觉深度学习目标检测航拍图像综述

Keywords

computer visiondeep learningobject detectionaerial imagereview

references

Abdelfattah R， Wang X F and Wang S. 2021. TTPLA： an aerial-image dataset for detection and segmentation of transmission towers and power lines//Proceedings of the 15th Asian Conference on Computer Vision. Kyoto， Japan： Springer： 601-618 ［DOI： 10.1007/978-3-030-69544-6_36http://dx.doi.org/10.1007/978-3-030-69544-6_36］

Adaimi G， Kreiss S and Alahi A. 2020. Perceiving traffic from aerial images ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2009.07611.pdfhttps://arxiv.org/pdf/2009.07611.pdf

Adelson E H， Anderson C H， Bergen J R， Burt P J and Ogden J M. 1984. Pyramid methods in image processing. RCA Engineer， 29（6）： 33-41

Albaba B M and Ozer S. 2021. SyNet： an ensemble network for object detection in UAV images//Proceedings of the 25th International Conference on Pattern Recognition. Milan， Italy： IEEE： 10227-10234 ［DOI： 10.1109/ICPR48806.2021.9412847http://dx.doi.org/10.1109/ICPR48806.2021.9412847］

Amudhan A N and Sudheer A P. 2022. Lightweight and computationally faster hypermetropic convolutional neural network for small size object detection. Image and Vision Computing， 119： #104396 ［DOI： 10.1016/j.imavis.2022.104396http://dx.doi.org/10.1016/j.imavis.2022.104396］

Azimi S M. 2019. ShuffleDet： real-time vehicle detection network in on-board embedded UAV imagery//Proceedings of 2019 European Conference on Computer Vision. Munich， Germany： Springer： 88-99 ［DOI： 10.1007/978-3-030-11012-3_7http://dx.doi.org/10.1007/978-3-030-11012-3_7］

Bochkovskiy A， Wang C Y and Liao H Y M. 2020. YOLOv4： optimal speed and accuracy of object detection ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2004.10934.pdfhttps://arxiv.org/pdf/2004.10934.pdf

Bouguettaya A， Zarzour H， Kechida A and Taberkit A M. 2022. Vehicle detection from UAV imagery with deep learning： a review. IEEE Transactions on Neural Networks and Learning Systems， 33（11）： 6047-6067 ［DOI： 10.1109/TNNLS.2021.3080276http://dx.doi.org/10.1109/TNNLS.2021.3080276］

Bozcan I and Kayacan E. 2020. AU-AIR： a multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance//Proceedings of 2020 IEEE International Conference on Robotics and Automation. Paris， France： IEEE： 8504-8510 ［DOI： 10.1109/ICRA40945.2020.9196845http://dx.doi.org/10.1109/ICRA40945.2020.9196845］

Božić-Štulić D， Marušić Ž and Gotovac S. 2019. Deep learning approach in aerial imagery for supporting land search and rescue missions. International Journal of Computer Vision， 127（9）： 1256-1278 ［DOI： 10.1007/s11263-019-01177-1http://dx.doi.org/10.1007/s11263-019-01177-1］

Bultmann S， Quenzel J and Behnke S. 2021. Real-time multi-modal semantic fusion on unmanned aerial vehicles//Proceedings of 2021 European Conference on Mobile Robots （ECMR）. Bonn， Germany： IEEE： 1-8 ［DOI： 10.1109/ECMR50962.2021.9568812http://dx.doi.org/10.1109/ECMR50962.2021.9568812］

Byun S， Shin I K， Moon J， Kang J and Choi S I. 2021. Road traffic monitoring from UAV images using deep learning networks. Remote Sensing， 13（20）： #4027 ［DOI： 10.3390/rs13204027http://dx.doi.org/10.3390/rs13204027］

Cai Y Q， Du D W， Zhang L B， Wen L W， Wang W Q， Wu Y J and Lyu Y S. 2019. Guided attention network for object detection and counting on drones ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1909.11307.pdfhttps://arxiv.org/pdf/1909.11307.pdf

Cai Z W and Vasconcelos N. 2018. Cascade R-CNN： delving into high quality object detection//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 6154-6162 ［DOI： 10.1109/CVPR.2018.00644http://dx.doi.org/10.1109/CVPR.2018.00644］

Cao J L， Cholakkal H， Anwer R M， Khan F S， Pang Y W and Shao L. 2020. D2Det： towards high quality object detection and instance segmentation//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 11482-11491 ［DOI： 10.1109/CVPR42600.2020.01150http://dx.doi.org/10.1109/CVPR42600.2020.01150］

Cao J L， Li Y L， Sun H Q， Xie J， Huang K Q and Pang Y W. 2022. A survey on deep learning based visual object detection. Journal of Image and Graphics， 27（6）： 1697-1722

曹家乐，李亚利，孙汉卿，谢今，黄凯奇，庞彦伟. 2022. 基于深度学习的视觉目标检测技术综述. 中国图象图形学报， 27（6）： 1697-1722 ［DOI： 10.11834/jig.220069http://dx.doi.org/10.11834/jig.220069］

Cao Y R， He Z J， Wang L J， Wang W Q， Yuan Y X， Zhang D W， Zhang J L， Zhu P F， Gool L V， Han J W， Hoi S， Hu Q H， Liu M， Cheng C， Liu F F， Cao G J， Li G Z， Wang H K， He J Y， Wan J F， Wan Q， Zhao Q， Lyu S， Zhao W Z， Lu X Q， Zhu X K， Liu Y J， Lyu Y X， Ma Y J， Yang Y T， Wang Z， Xu Z Y， Luo Z P， Zhang Z M， Zhang Z G， Li Z H and Zhang Z X. 2021. VisDrone-DET2021： the vision meets drone object detection Challenge results//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops （ICCVW）. Montreal， Canada： IEEE： 2847-2854 ［DOI： 10.1109/ICCVW54120.2021.00319http://dx.doi.org/10.1109/ICCVW54120.2021.00319］

Carion N， Massa F， Synnaeve G， Usunier N， Kirillov A and Zagoruyko S. 2020. End-to-end object detection with transformers//Proceedings of the 16th European Conference on Computer Vision. Cham. Glasgow， UK： Springer： 213-229 ［DOI： 10.1007/978-3-030-58452-8_13http://dx.doi.org/10.1007/978-3-030-58452-8_13］

Chang Y C， Chen H T， Chuang J H and Liao I C. 2018. Pedestrian detection in aerial images using vanishing point transformation and deep learning//Proceedings of the 25th IEEE International Conference on Image Processing. Athens， Greece： IEEE： 1917-1921 ［DOI： 10.1109/ICIP.2018.8451144http://dx.doi.org/10.1109/ICIP.2018.8451144］

Chen C R， Zhang Y， Lyu Q X， Wei S， Wang X R， Sun X and Dong J Y. 2019a. RRNet： a hybrid detector for object detection in drone-captured images//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South）： 100-108 ［DOI： 10.1109/ICCVW.2019.00018http://dx.doi.org/10.1109/ICCVW.2019.00018］

Chen G， Wang H T， Chen K， Li Z J， Song Z D， Liu Y L， Chen W K. and Knoll A， 2020. A survey of the four pillars for small object detection： multiscale representation， contextual information， super-resolution， and region proposal. IEEE Transactions on systems， man， and cybernetics： systems， 52（2）， 936-953 ［DOI： 10.1109/TSMC.2020.3005231http://dx.doi.org/10.1109/TSMC.2020.3005231］

Chen Y， Lee W S， Gan H， Peres N， Fraisse C， Zhang Y C and He Y. 2019b. Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sensing， 11（13）： #1584 ［DOI： 10.3390/rs11131584http://dx.doi.org/10.3390/rs11131584］

Chen Y F， Zheng W Q， Zhao Y Y， Song T H and Shin H. 2023. DW-YOLO： An efficient object detector for drones and self-driving vehicles. Arabian Journal for Science and Engineering， 48（2）： 1427-1436 ［DOI： 10.1007/s13369-022-06874-7http://dx.doi.org/10.1007/s13369-022-06874-7］

Chen Y T， Li J， Niu Y F and He J B. 2019c. Small object detection networks based on classification-oriented super-resolution GAN for UAV aerial imagery//Proceedings of 2019 Chinese Control and Decision Conference. Nanchang， China： IEEE： 4610-4615 ［DOI： 10.1109/CCDC.2019.8832735http://dx.doi.org/10.1109/CCDC.2019.8832735］

Dai J F， Li Y， He K M and Sun J. 2016. R-FCN： object detection via region-based fully convolutional networks//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona， Spain： Curran Associates Inc.： 379-387

Dalal N and Triggs B. 2005. Histograms of oriented gradients for human detection//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego， USA： IEEE： 886-893 ［DOI： 10.1109/CVPR.2005.177http://dx.doi.org/10.1109/CVPR.2005.177］

Deng S T， Li S， Xie K， Song W F， Liao X， Hao A M and Qin H. 2021. A global-local self-adaptive network for drone-view object detection. IEEE Transactions on Image Processing， 30： 1556-1569 ［DOI： 10.1109/TIP.2020.3045636http://dx.doi.org/10.1109/TIP.2020.3045636］

DeVries T and Taylor G W. 2017. Improved regularization of convolutional neural networks with cutout ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1708.04552.pdfhttps://arxiv.org/pdf/1708.04552.pdf

Domozi Z， Stojcsics D， Benhamida A， Kozlovszky M and Molnar A. 2020. Real time object detection for aerial search and rescue missions for missing persons//Proceedings of the 15th IEEE International Conference of System of Systems Engineering. Budapest， Hungary： IEEE： 519-524 ［DOI： 10.1109/SoSE50414.2020.9130475http://dx.doi.org/10.1109/SoSE50414.2020.9130475］

Dong J， Ota K and Dong M X. 2021. UAV-based real-time survivor detection system in post-disaster search and rescue operations. IEEE Journal on Miniaturization for Air and Space Systems， 2（4）： 209-219 ［DOI： 10.1109/JMASS.2021.3083659http://dx.doi.org/10.1109/JMASS.2021.3083659］

Du D W， Qi Y K， Yu H Y， Yang Y F， Duan K W， Li G R， Zhang W G， Huang Q M and Tian Q. 2018. The unmanned aerial vehicle benchmark： object detection and tracking//Proceedings of the 15th European conference on computer vision. Munich， Germany： Springer： 375-391 ［DOI： 10.1007/978-3-030-01249-6_23http://dx.doi.org/10.1007/978-3-030-01249-6_23］

Duan C Z， Wei Z W， Zhang C， Qu S Y and Wang H P. 2021. Coarse-grained density map guided object detection in aerial images//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops （ICCVW）. Montreal， Canada： IEEE： 2789-2798 ［DOI： 10.1109/ICCVW54120.2021.00313http://dx.doi.org/10.1109/ICCVW54120.2021.00313］

Duan K W， Bai S， Xie L X， Qi H G， Huang Q M and Tian Q. 2019. CenterNet： keypoint triplets for object detection//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 6568-6577 ［DOI： 10.1109/ICCV.2019.00667http://dx.doi.org/10.1109/ICCV.2019.00667］

Fang Q Y， Han D P and Wang Z K. 2021. Cross-modality fusion transformer for multispectral object detection ［EB/OL］. ［2022-10-04］. https://arxiv.org/pdf/2111.00273.pdfhttps://arxiv.org/pdf/2111.00273.pdf

Gao M F， Yu R C， Li A， Morariu V I and Davis L S. 2018. Dynamic zoom-in network for fast object detection in large images//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 6926-6935 ［DOI： 10.1109/CVPR.2018.00724http://dx.doi.org/10.1109/CVPR.2018.00724］

Girshick R. 2015. Fast R-CNN//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago， Chile： IEEE： 1440-1448 ［DOI： 10.1109/ICCV.2015.169http://dx.doi.org/10.1109/ICCV.2015.169］

Girshick R， Donahue J， Darrell T and Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus， USA： IEEE： 580-587 ［DOI： 10.1109/CVPR.2014.81http://dx.doi.org/10.1109/CVPR.2014.81］

Gong C Y， Wang D L， Li M， Chandra V and Liu Q. 2021. KeepAugment： a simple information-preserving data augmentation approach//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 1055-1064 ［DOI： 10.1109/CVPR46437.2021.00111http://dx.doi.org/10.1109/CVPR46437.2021.00111］

Gong Z H and Li D. 2020. Towards better object detection in scale variation with adaptive feature selection ［EB/OL］. ［2022-09-08］. https://arxiv.org/pdf/2012.03265.pdfhttps://arxiv.org/pdf/2012.03265.pdf

He K M， Gkioxari G， Doll􀆦r P and Girshick R. 2017. Mask R-CNN//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 2980-2988 ［DOI： 10.1109/ICCV.2017.322http://dx.doi.org/10.1109/ICCV.2017.322］

Hong S， Kang S and Cho D. 2019. Patch-level augmentation for object detection in aerial images//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South）： IEEE： 127-134 ［DOI： 10.1109/ICCVW.2019.00021http://dx.doi.org/10.1109/ICCVW.2019.00021］

Howard A， Sandler M， Chen B， WANG W J， Chen L C， Tan M X， Chu G， Vasudevan V， Zhu Y K， Pang R M， Adam H and Le Q. 2019. Searching for mobileNetV3//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 1314-1324 ［DOI： 10.1109/ICCV.2019.00140http://dx.doi.org/10.1109/ICCV.2019.00140］

Howard A G， Zhu M L， Chen B， Kalenichenko D， Wang W J， Weyand T and Adam H. 2017. MobileNets： efficient convolutional neural networks for mobile vision applications ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1704.04861.pdfhttps://arxiv.org/pdf/1704.04861.pdf

Hsieh M R， Lin Y L and Hsu W H. 2017. Drone-based object counting by spatially regularized regional proposal network//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 4145-4153 ［DOI： 10.1109/ICCV.2017.446http://dx.doi.org/10.1109/ICCV.2017.446］

Huang S H， Lu Z C， Cheng R and He C. 2021. FaPN： feature-aligned pyramid network for dense image prediction//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 844-853 ［DOI： 10.1109/ICCV48922.2021.00090http://dx.doi.org/10.1109/ICCV48922.2021.00090］

Huang Y C， Chen J X and Huang D. 2022. UFPMP-Det： toward accurate and efficient object detection on drone imagery//Proceedings of 2022 AAAI Conference on Artificial Intelligence California， USA： AAAI： 1016-1033 ［DOI： 10.1609/aaai.v36i1.19986http://dx.doi.org/10.1609/aaai.v36i1.19986］

Iandola F N， Han S， Moskewicz M W， Ashraf K， Dally W J and Keutzer K. 2016. SqueezeNet： AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1602.07360.pdfhttps://arxiv.org/pdf/1602.07360.pdf

Iversen N， Schofield O B， Cousin L， Ayoub N， Vom Bögel G and Ebeid E. 2021. Design， integration and implementation of an intelligent and self-recharging drone system for autonomous power line inspection//Proceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. Prague， Czech Republic： IEEE： 4168-4175 ［DOI： 10.1109/IROS51168.2021.9635924http://dx.doi.org/10.1109/IROS51168.2021.9635924］

Jalil B， Leone G R， Martinelli M， Moroni D， Pascali M A and Berton A. 2019. Fault detection in power equipment via an unmanned aerial system using multi modal data. Sensors， 19（13）： #3014 ［DOI： 10.3390/s19133014http://dx.doi.org/10.3390/s19133014］

Ji S W， Yang X W， Huang J Y and Yin N. 2019. Model compression method of convolution neural network based on feature-reuse. Journal of Computer Applications， 39（6）： 1607-1613

冀树伟，杨喜旺，黄晋英，尹宁. 2019. 基于特征复用的卷积神经网络模型压缩方法. 计算机应用， 39（6）： 1607-1613 ［DOI： 10.11772lj.issn.1001-9081.2018091992http://dx.doi.org/10.11772lj.issn.1001-9081.2018091992］

Jiang B， Qu R K， Li Y D and Li C L. 2021. Object detection in UAV imagery based on deep learning： review. Acta Aeronautica et Astronautica Sinica， 42（4）： #524519

江波，屈若锟，李彦冬，李诚龙. 2021. 基于深度学习的无人机航拍目标检测研究综述. 航空学报， 42（4）： #524519 ［DOI： 10.7527s/s1000-6893.2020.24519http://dx.doi.org/10.7527s/s1000-6893.2020.24519］

Jiang N， Yu X H， Peng X K， Gong Y Q and Han Z J. 2021. SM+： refined scale match for tiny person detection//Proceedings of 2021 IEEE International Conference on Acoustics， Speech and Signal Processing. Toronto， Canada： IEEE： 1815-1819 ［DOI： 10.1109/ICASSP39728.2021.9414162http://dx.doi.org/10.1109/ICASSP39728.2021.9414162］

Jin L L， Yang W Z， Wang S L， Cui Z C， Chen X Y and Chen L P. 2018. Mixed pruning method for convolutional neural network compression. Journal of Chinese Computer Systems， 39（12）： 2596-2601

靳丽蕾，杨文柱，王思乐，崔振超，陈向阳，陈丽萍. 2018. 一种用于卷积神经网络压缩的混合剪枝方法. 小型微型计算机系统， 39（12）： 2596-2601 ［DOI： 10.3969/j.issn.1000-1220.2018.12.007http://dx.doi.org/10.3969/j.issn.1000-1220.2018.12.007］

Kiefer B， Messmer M and Zell A. 2021a. Diminishing domain bias by leveraging domain labels in object detection on UAVs//Proceedings of the 20th International Conference on Advanced Robotics. Ljubljana， Slovenia： IEEE： 523-530 ［DOI： 10.1109/ICAR53236.2021.9659357http://dx.doi.org/10.1109/ICAR53236.2021.9659357］

Kiefer B， Ott D and Zell A. 2021b. Leveraging synthetic data in object detection on unmanned aerial vehicles ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2112.12252.pdfhttps://arxiv.org/pdf/2112.12252.pdf

Kisantal M， Wojna Z， Murawski J， Naruniec J and Cho K. 2019. Augmentation for small object detection ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1902.07296.pdfhttps://arxiv.org/pdf/1902.07296.pdf

Kong T， Sun F C， Yao A B， Liu H P， Lu M and Chen Y R. 2017. RON： reverse connection with objectness prior networks for object detection//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 5244-5252 ［DOI： 10.1109/CVPR.2017.557http://dx.doi.org/10.1109/CVPR.2017.557］

Ledig C， Theis L， Husz􀆦r F， Caballero J， Cunningham A， Acosta A， Aitken A， Tejani A， Totz J， Wang Z H and Shi W Z. 2017. Photo-realistic single image super-resolution using a generative adversarial network//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 105-114 ［DOI： 10.1109/CVPR.2017.19http://dx.doi.org/10.1109/CVPR.2017.19］

Leng J X， Mo M J C， Zhou Y H， Gao C Q， Li W S and Gao X B. 2023. Pareto refocusing for drone-view object detection. IEEE Transactions on Circuits and Systems for Video Technology， 33（3）： 1320-1334 ［DOI： 10.1109/TCSVT.2022.3210207http://dx.doi.org/10.1109/TCSVT.2022.3210207］

Li C L， Yang T J N， Zhu S J， Chen C and Guan S Y. 2020. Density map guided object detection in aerial images//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle， USA： IEEE： 737-746 ［DOI： 10.1109/CVPRW50498.2020.00103http://dx.doi.org/10.1109/CVPRW50498.2020.00103］

Li J， Ye D H， Kolsch M， Wachs J P and Bouman C A. 2022. Fast and robust UAV to UAV detection and tracking from video. IEEE Transactions on Emerging Topics in Computing， 10（3）： 1519-1531 ［DOI： 10.1109/TETC.2021.3104555http://dx.doi.org/10.1109/TETC.2021.3104555］

Li W， Li H L， Wu Q B， Chen X Y and Ngan K N. 2019. Simultaneously detecting and counting dense vehicles from drone images. IEEE Transactions on Industrial Electronics， 66（12）： 9651-9662 ［DOI： 10.1109/TIE.2019.2899548http://dx.doi.org/10.1109/TIE.2019.2899548］

Li W， Wei W and Zhang L. 2021. GSDet： object detection in aerial images based on scale reasoning. IEEE Transactions on Image Processing， 30： 4599-4609 ［DOI： 10.1109/TIP.2021.3073319http://dx.doi.org/10.1109/TIP.2021.3073319］

Li Z X and Zhou F Q. 2017. FSSD： feature fusion single shot Multibox detector ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1712.00960.pdfhttps://arxiv.org/pdf/1712.00960.pdf

Liang X， Zhang J， Zhuo L， Li Y Z and Tian Q. 2020. Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis. IEEE Transactions on Circuits and Systems for Video Technology， 30（6）： 1758-1770 ［DOI： 10.1109/TCSVT.2019.2905881http://dx.doi.org/10.1109/TCSVT.2019.2905881］

Liao J J， Piao Y， Su J H， Cai G R， Huang X W， Chen L， Huang Z H and Wu Y D. 2021. Unsupervised cluster guided object detection in aerial images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 14： 11204-11216 ［DOI： 10.1109/JSTARS.2021.3122152http://dx.doi.org/10.1109/JSTARS.2021.3122152］

Lin Q Z， Ding Y， Xu H， Lin W X， Li J X and Xie X X. 2021. ECascade-RCNN： enhanced cascade RCNN for multi-scale object detection in UAV images//Proceedings of the 7th International Conference on Automation， Robotics and Applications. Prague， Czech Republic： IEEE： 268-272 ［DOI： 10.1109/ICARA51699.2021.9376456http://dx.doi.org/10.1109/ICARA51699.2021.9376456］

Lin T Y， Doll􀆦r P， Girshick R， He K M， Hariharan B and Belongie S. 2017a. Feature pyramid networks for object detection//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 936-944 ［DOI： 10.1109/CVPR.2017.106http://dx.doi.org/10.1109/CVPR.2017.106］

Lin T Y， Goyal P， Girshick R， He K M and Doll􀆦r P. 2017b. Focal loss for dense object detection//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 2999-3007 ［DOI： 10.1109/ICCV.2017.324http://dx.doi.org/10.1109/ICCV.2017.324］

Liu M J， Wang X H， Zhou A J， Fu X Y， Ma Y W and Piao C. 2020. UAV-YOLO： small object detection on unmanned aerial vehicle perspective. Sensors， 20（8）： #2238 ［DOI： 10.3390/s20082238http://dx.doi.org/10.3390/s20082238］

Liu W， Anguelov D， Erhan D， Szegedy C， Reed S， Fu C Y and Berg A C. 2016. SSD： single shot multiBox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 21-37 ［DOI： 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2］

Liu X and Zhang Z Y. 2021. A vision-based target detection， tracking， and positioning algorithm for unmanned aerial Vehicle. Wireless Communications and Mobile Computing， 2021： #5565589 ［DOI： 10.1155/2021/5565589http://dx.doi.org/10.1155/2021/5565589］

Liu Z M， Gao G Y， Sun L and Fang Z Y. 2021. HRDNet： high-resolution detection network for small objects//Proceedings of 2021 IEEE International Conference on Multimedia and Expo. Shenzhen， China： IEEE： 1-6 ［DOI： 10.1109/ICME51207.2021.9428241http://dx.doi.org/10.1109/ICME51207.2021.9428241］

Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision， 60（2）： 91-110 ［DOI： 10.1023/B：VISI.0000029664.99615.94http://dx.doi.org/10.1023/B：VISI.0000029664.99615.94］

Lu Y X， Javidi T and Lazebnik S. 2016. Adaptive object detection using adjacency and zoom prediction//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 2351-2359 ［DOI： 10.1109/CVPR.2016.258http://dx.doi.org/10.1109/CVPR.2016.258］

Lyu R R， Wang X and Yang T W. 2020. Small object detection with scale adaptive balance mechanism//Proceedings of the 15th IEEE International Conference on Signal Processing. Beijing， China： IEEE： 361-365 ［DOI： 10.1109/ICSP48669.2020.9320999http://dx.doi.org/10.1109/ICSP48669.2020.9320999］

Ma N N， Zhang X Y， Zheng H T and Sun J. 2018. ShuffleNet V2： practical guidelines for efficient CNN architecture design//Proceedings of 2018 European Conference on Computer Vision. Munich， Germany： Springer： 122-138 ［DOI： 10.1007/978-3-030-01264-9_8http://dx.doi.org/10.1007/978-3-030-01264-9_8］

Mandal M， Kumar L K and Vipparthi S K. 2020. MOR-UAV： a benchmark dataset and baselines for moving object recognition in UAV videos//Proceedings of the 28th ACM International Conference on Multimedia. Seattle， USA： ACM： 2626-2635 ［DOI： 10.1145/3394171.3413934http://dx.doi.org/10.1145/3394171.3413934］

Messmer M， Kiefer B and Zell A. 2021. Gaining scale invariance in UAV bird’s eye view object detection by adaptive resizing ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2101.12694.pdfhttps://arxiv.org/pdf/2101.12694.pdf

Mittal P， Singh R and Sharma A. 2020. Deep learning-based object detection in low-altitude UAV datasets： a survey. Image and Vision Computing， 104： #104046 ［DOI： 10.1016/j.imavis.2020.104046http://dx.doi.org/10.1016/j.imavis.2020.104046］

Oksuz K， Cam B C， Kalkan S and Akbas E. 2021. Imbalance problems in object detection： a review. IEEE Transactions on Pattern Analysis and Machine Intelligence， 43（10）： 3388-3415 ［DOI： 10.1109/TPAMI.2020.2981890http://dx.doi.org/10.1109/TPAMI.2020.2981890］

Osco L P， De Arruda M D S， Goncalves D N， Dias A， Batistoti J， De Souza M， Gomes F D G， Ramos A P M， Jorge L A D C， Liesenberg V， Li J， Ma L F， Junior J M and Goncalves W N. 2021. A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery. ISPRS Journal of Photogrammetry and Remote Sensing， 174： 1-17 ［DOI： 10.1016/j.isprsjprs.2021.01.024http://dx.doi.org/10.1016/j.isprsjprs.2021.01.024］

Perdana M I， Risnumawan A and Sulistijono I A. 2020. Automatic aerial victim detection on low-cost thermal camera using convolutional neural network//2020 International Symposium on Community-centric Systems. Tokyo， Japan： IEEE： 1-5 ［DOI： 10.1109/CcS49175.2020.9231433http://dx.doi.org/10.1109/CcS49175.2020.9231433］

Pi Z L， Lian Y C， Chen X E， Wu Y N， Li Y P and Jiao L C. 2019. A novel spatial and temporal context-aware approach for drone-based video object detection//2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South）： IEEE： 179-188 ［DOI： 10.1109/ICCVW.2019.00027http://dx.doi.org/10.1109/ICCVW.2019.00027］

Qiao S Y， Chen L C and Yuille A. 2021. DetectoRS： detecting objects with recursive feature pyramid and switchable Atrous convolution//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 10208-10219 ［DOI： 10.1109/CVPR46437.2021.01008http://dx.doi.org/10.1109/CVPR46437.2021.01008］

Queralta J P， Raitoharju J， Gia T N， Passalis N and Westerlund T. 2020. AutoSOS： towards multi-UAV systems supporting maritime search and rescue with lightweight AI and edge computing ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2005.03409.pdfhttps://arxiv.org/pdf/2005.03409.pdf

Redmon J， Divvala S， Girshick R and Farhadi A. 2016. You only look once： unified， real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 779-788 ［DOI： 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91］

Redmon J and Farhadi A. 2017. YOLO9000： better， faster， stronger//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 6517-6525 ［DOI： 10.1109/CVPR.2017.690http://dx.doi.org/10.1109/CVPR.2017.690］

Ren S Q， He K M， Girshick R and Sun J. 2015. Faster R-CNN： towards real-time object detection with region proposal networks//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal， Canada： MIT Press： 91-99

Ringwald T， Sommer L， Schumann A， Beyerer J and Stiefelhagen R. 2019. UAV-Net： a fast aerial vehicle detector for mobile platforms//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 544-552 ［DOI： 10.1109/CVPRW.2019.00080http://dx.doi.org/10.1109/CVPRW.2019.00080］

Rizk M， Slim F and Charara J. 2021. Toward AI-assisted UAV for human detection in Search and rescue missions//Proceedings of 2021 International Conference on Decision Aid Sciences and Application. Sakheer， Bahrain： IEEE： 781-786 ［DOI： 10.1109/DASA53625.2021.9682412http://dx.doi.org/10.1109/DASA53625.2021.9682412］

Sambolek S and Ivašić-Kos M. 2020. Detecting objects in drone imagery： a brief overview of recent progress//Proceedings of the 43rd International Convention on Information， Communication and Electronic Technology. Opatija， Croatia： IEEE： 1052-1057 ［DOI： 10.23919/MIPRO48935.2020.9245321http://dx.doi.org/10.23919/MIPRO48935.2020.9245321］

Sandler M， Howard A， Zhu M L， Zhmoginov A and Chen L C. 2018. MobileNetV2： inverted residuals and linear bottlenecks//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 4510-4520 ［DOI： 10.1109/CVPR.2018.00474http://dx.doi.org/10.1109/CVPR.2018.00474］

Shah S， Dey D， Lovett C and Kapoor A. 2018. AirSim： high-fidelity visual and physical simulation for autonomous vehicles//Proceedings of the 11th Field and Service Robotics. Cham： Springer： 621-635 ［DOI： 10.1007/978-3-319-67361-5_40http://dx.doi.org/10.1007/978-3-319-67361-5_40］

Shams T and Desbarats P. 2020. Detection of ASIAN hornet’s nest on drone acquired FLIR and color images using deep learning methods//Proceedings of 2020 Tenth International Conference on Image Processing Theory， Tools and Applications. Paris， France： IEEE： 1-6 ［DOI： 10.1109/IPTA50016.2020.9286693http://dx.doi.org/10.1109/IPTA50016.2020.9286693］

Shaniya P， Jati G， Alhamidi M R， Caesarendra W and Jatmiko W. 2021. YOLOv4 RGBT human detection on unmanned aerial vehicle perspective//Proceedings of the 6th International Workshop on Big Data and Information Security （IWBIS）. Depok， Indonesia： IEEE： 41-46 ［DOI： 10.1109/IWBIS53353.2021.9631856http://dx.doi.org/10.1109/IWBIS53353.2021.9631856］

Shihavuddin A S M， Chen X， Fedorov V， Christensen A N， Riis N A B， Branner K， Dahl A B and Paulsen R P. 2019. Wind turbine surface damage detection by deep learning aided drone inspection analysis. Energies， 12（4）： #676 ［DOI： 10.3390/en12040676http://dx.doi.org/10.3390/en12040676］

Shrivastava A， Gupta A and Girshick R. 2016. Training region-based object detectors with online hard example mining//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 761-769 ［DOI： 10.1109/CVPR.2016.89http://dx.doi.org/10.1109/CVPR.2016.89］

Singh B and Davis L S. 2018. An analysis of scale invariance in object detection-SNIP//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 3578-3587 ［DOI： 10.1109/CVPR.2018.00377http://dx.doi.org/10.1109/CVPR.2018.00377］

Singh B， Najibi M and Davis L S. 2018. SNIPER： efficient multi-scale training//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal， Canada： Curran Associates Inc.： 9333-9343

Srivastava S， Narayan S and Mittal S. 2021. A survey of deep learning techniques for vehicle detection from UAV images. Journal of Systems Architecture， 117： #102152 ［DOI： 10.1016/j.sysarc.2021.102152http://dx.doi.org/10.1016/j.sysarc.2021.102152］

Sun Y M， Cao B， Zhu P F and Hu Q H. 2022. Drone-based RGB-Infrared cross-modality vehicle detection via uncertainty-aware learning. IEEE Transactions on Circuits and Systems for Video Technology， 32（10）： 6700-6713 ［DOI： 10.1109/TCSVT.2022.3168279http://dx.doi.org/10.1109/TCSVT.2022.3168279］

Tan M X， Pang R M and Le Q V. 2020. EfficientDet： scalable and efficient object detection//Proceedings of 2020 IEEE/CVF conference on computer vision and pattern recognition. Seattle， USA： IEEE： 10778-10787 ［DOI： 10.1109/CVPR42600.2020.01079http://dx.doi.org/10.1109/CVPR42600.2020.01079］

Tang W Q， Sun J and Wang G. 2021. Horizontal feature pyramid network for object detection in UAV images//Proceedings of 2021 China Automation Congress. Beijing， China： IEEE： 7746-7750 ［DOI： 10.1109/CAC53003.2021.9727887http://dx.doi.org/10.1109/CAC53003.2021.9727887］

Tang Z Y， Liu X and Yang B J. 2020. PENet： object detection using points estimation in high definition aerial images//Proceedings of the 19th IEEE International Conference on Machine Learning and Applications. Miami， USA： IEEE： 392-398 ［DOI： 10.1109/ICMLA51294.2020.00069http://dx.doi.org/10.1109/ICMLA51294.2020.00069］

Tian Z， Shen C H， Chen H and He T. 2019. FCOS： fully convolutional one-stage object detection//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 9626-9635 ［DOI： 10.1109/ICCV.2019.00972http://dx.doi.org/10.1109/ICCV.2019.00972］

Ünel F Ö， Özkalayci B O and Çiğla C. 2019. The power of tiling for small object detection//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 582-591 ［DOI： 10.1109/CVPRW.2019.00084http://dx.doi.org/10.1109/CVPRW.2019.00084］

Varga L A， Kiefer B， Messmer M and Zell A. 2022. SeaDronesSee： a maritime benchmark for detecting humans in open water//Proceedings of 2022 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa， USA： IEEE： 3686-3696 ［DOI： 10.1109/WACV51458.2022.00374http://dx.doi.org/10.1109/WACV51458.2022.00374］

Vemula S and Frye M. 2020. Mask R-CNN powerline detector： a deep learning approach with applications to a UAV//Proceedings of the 39th AIAA/IEEE Digital Avionics Systems Conference. San Antonio， USA： IEEE： 1-6 ［DOI： 10.1109/DASC50938.2020.9256456http://dx.doi.org/10.1109/DASC50938.2020.9256456］

Vieira-e-Silva A L B， Felix H D C， Chaves T D M， Simões F P M， Teichrieb V， Santos M M D， Santiago H D C， Sgotti V A C and Neto H B D T L. 2021. STN PLAD： a dataset for multi-size power line assets detection in high-resolution UAV images//Proceedings of the 34th SIBGRAPI Conference on Graphics， Patterns and Images. Gramado， Brazil： IEEE： 215-222 ［DOI： 10.1109/SIBGRAPI54419.2021.00037http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00037］

Wang J W， Yang W， Guo H W， Zhang R X and Xia G S. 2021. Tiny object detection in aerial images//Proceedings of the 25th International Conference on Pattern Recognition. Milan， Italy： IEEE： 3791-3798 ［DOI： 10.1109/ICPR48806.2021.9413340http://dx.doi.org/10.1109/ICPR48806.2021.9413340］

Wang Y， Yang Y L and Zhao X. 2020. Object detection using clustering algorithm adaptive searching regions in aerial images//Proceedings of 2022 European Conference on Computer Vision. Glasgow， UK： Springer： 651-664 ［DOI： 10.1007/978-3-030-66823-5_39http://dx.doi.org/10.1007/978-3-030-66823-5_39］

Wei Z W， Duan C Z， Song X H， Tian Y and Wang H P. 2020. AMRNET： chips augmentation in aerial images object detection ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2009.07168.pdfhttps://arxiv.org/pdf/2009.07168.pdf

Wen L Y， Du D W， Zhu P F， Hu Q H， Wang Q L， Bo L F and Yu S W. 2021. Detection， tracking， and counting meets drones in crowds： a benchmark//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 7808-7817 ［DOI： 10.1109/CVPR46437.2021.00772http://dx.doi.org/10.1109/CVPR46437.2021.00772］

Wittstruck L， Kühling I， Trautz D， Kohlbrecher M and Jarmer T. 2020. UAV-based RGB imagery for Hokkaido pumpkin （Cucurbita max.） detection and yield estimation. Sensors， 21（1）： #118 ［DOI： 10.3390/s21010118http://dx.doi.org/10.3390/s21010118］

Wu H H， Zhou Z J， Feng M， Yan Y Z， Xu H and Qian L J. 2019a. Real-time single object detection on the UAV//Proceedings of 2019 International Conference on Unmanned Aircraft Systems. Atlanta， USA： IEEE： 1013-1022 ［DOI： 10.1109/ICUAS.2019.8797866http://dx.doi.org/10.1109/ICUAS.2019.8797866］

Wu Z Y， Suresh K， Narayanan P， Xu H Y， Kwon H and Wang Z Y. 2019b. Delving into robust object detection from unmanned aerial vehicles： a deep nuisance disentanglement approach//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 1201-1210 ［DOI： 10.1109/ICCV.2019.00129http://dx.doi.org/10.1109/ICCV.2019.00129］

Xiao J S， Zhang S H， Dai Y， Jiang Z J， Yi B S and Xu C. 2020. Multiclass object detection in UAV images based on rotation region network. IEEE Journal on Miniaturization for Air and Space Systems， 1（3）： 188-196. ［DOI： 10.1109/JMASS.2020.3025970http://dx.doi.org/10.1109/JMASS.2020.3025970］

Xiong Y Y， Liu H X， Gupta S， Akin B， Bender G， Wang Y Z， Kindermans P J， Tan M X， Singh V and Chen B. 2021. MobileDets： searching for object detection architectures for mobile accelerators//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 3824-3833 ［DOI： 10.1109/CVPR46437.2021.00382http://dx.doi.org/10.1109/CVPR46437.2021.00382］

Xu C， Wang J W， Yang W and Yu L. 2021a. Dot distance for tiny object detection in aerial images//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Nashville， USA： IEEE： 1192-1201 ［DOI： 10.1109/CVPRW53098.2021.00130http://dx.doi.org/10.1109/CVPRW53098.2021.00130］

Xu J T， Li Y L and Wang S J. 2021b. AdaZoom： adaptive zoom network for multi-scale object detection in large scenes ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2106.10409.pdfhttps://arxiv.org/pdf/2106.10409.pdf

Yang C H Y， Huang Z H and Wang N Y. 2021. QueryDet： cascaded sparse query for accelerating high-resolution small object detection//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashviuea， USA： IEEE： 7808-7817 ［DOI：10.1109/CVPR52688.2022.01330http://dx.doi.org/10.1109/CVPR52688.2022.01330］

Yang F， Fan H， Chu P， Blasch E and Ling H B. 2019a. Clustered object detection in aerial images//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 8310-8319 ［DOI： 10.1109/ICCV.2019.00840http://dx.doi.org/10.1109/ICCV.2019.00840］

Yang H R and Zhang Y H. 2022a. Review of UAV target detection algorithms based on computer vision. Electronic Test， 36（4）： 44-45， 36

杨浩然，张雨晗. 2022a. 基于计算机视觉的无人机目标检测算法综述. 电子测试， 36（4）： 44-45， 36 ［DOI： 10.16520/j.cnki.1000-8519.2022.04.006http://dx.doi.org/10.16520/j.cnki.1000-8519.2022.04.006］

Yang L Z， Ma R H and Zakhor A. 2022b. Drone object detection using RGB/IR fusion ［EB/OL］. ［2022-09-06］. https://arxiv.org/pdf/2201.03786v1.pdfhttps://arxiv.org/pdf/2201.03786v1.pdf

Yang X， Yang J R， Yan J C， Zhang Y， Zhang T F， Guo Z and Fu K. 2019b. SCRDet： towards more robust detection for small， cluttered and rotated objects//Proceedings of 2019 IEEE/CVF International Conference on Computer. Seoul， Korea （South）： IEEE： 8231-8240 ［DOI： 10.1109/ICCV.2019.00832http://dx.doi.org/10.1109/ICCV.2019.00832］

Yu G H， Chang Q Y， Lyu W Y， Xu C， Cui C， Ji W， Dang Q Q， Deng K P， Wang G Z， Du Y N， Lai B H， Liu Q W， Hu X G， Yu D H and Ma Y. 2021a. PP-PicoDet： a better real-time object detector on mobile devices ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/2111.00902.pdfhttps://arxiv.org/pdf/2111.00902.pdf

Yu W P， Yang T J N and Chen C. 2021b. Towards resolving the challenge of long-tail distribution in UAV images for object detection//Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision. Waikoloa， USA： IEEE： 3257-3266 ［DOI： 10.1109/WACV48630.2021.00330http://dx.doi.org/10.1109/WACV48630.2021.00330］

Yu X H， Gong Y Q， Jiang N， Ye Q X and Han Z J. 2020. Scale match for tiny person detection//Proceedings of 2020 IEEE Winter Conference on Applications of Computer Vision. Snowmass， USA： IEEE： 1246-1254 ［DOI： 10.1109/WACV45572.2020.9093394http://dx.doi.org/10.1109/WACV45572.2020.9093394］

Yuan W A and Choi D. 2021. UAV-based heating requirement determination for frost management in apple orchard. Remote Sensing， 13（2）： #273 ［DOI： 10.3390/rs13020273http://dx.doi.org/10.3390/rs13020273］

Yun S， Han D， Chun S， Oh S J， Yoo Y and Choe J. 2019. CutMix： regularization strategy to train strong classifiers with localizable features//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 6022-6031 ［DOI： 10.1109/ICCV.2019.00612http://dx.doi.org/10.1109/ICCV.2019.00612］

Zhang H， Yang W， Yu H， Zhang H J and Xia G S. 2019a. Detecting power lines in UAV images with convolutional features and structured constraints. Remote Sensing， 11（11）： #1342 ［DOI： 10.3390/rs11111342http://dx.doi.org/10.3390/rs11111342］

Zhang H Y， Cisse M， Dauphin Y N and Lopez-Paz D. 2017. mixup： beyond empirical risk minimization ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1710.09412.pdfhttps://arxiv.org/pdf/1710.09412.pdf

Zhang J Y， Huang J Y， Chen X K and Zhang D Y. 2019b. How to fully exploit the abilities of aerial image detectors//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South）： IEEE： 1-8 ［DOI： 10.1109/ICCVW.2019.00007http://dx.doi.org/10.1109/ICCVW.2019.00007］

Zhang P Y， Zhong Y X and Li X Q. 2019c. SlimYOLOv3： narrower， faster and better for real-time UAV applications//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshops. Seoul， Korea （South）： IEEE： 37-45 ［DOI： 10.1109/ICCVW.2019.00011http://dx.doi.org/10.1109/ICCVW.2019.00011］

Zhang X D， Izquierdo E and Chandramouli K. 2019d. Dense and small object detection in UAV vision based on cascade network//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South）： IEEE： 118-126 ［DOI： 10.1109/ICCVW.2019.00020http://dx.doi.org/10.1109/ICCVW.2019.00020］

Zhang X Y， Zhou X Y， Lin M X and Sun J. 2018. ShuffleNet： an extremely efficient convolutional neural network for mobile devices//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 6848-6856 ［DOI： 10.1109/CVPR.2018.00716http://dx.doi.org/10.1109/CVPR.2018.00716］

Zhang Y Y， Zhou D S， Chen S Q， Gao S H and Ma Y. 2016. Single-image crowd counting via multi-column convolutional neural network//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 589-597 ［DOI： 10.1109/CVPR.2016.70http://dx.doi.org/10.1109/CVPR.2016.70］

Zhang Z X， Lu X Q， Cao G J， Yang Y T， Jiao L C and Liu F. 2021. ViT-YOLO： transformer-based YOLO for object detection//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops （ICCVW）. Montreal， Canada： IEEE： 2799-2808 ［DOI： 10.1109/ICCVW54120.2021.00314http://dx.doi.org/10.1109/ICCVW54120.2021.00314］

Zhao H S， Shi J P， Qi X J， Wang X G and Jia J Y. 2017. Pyramid scene parsing network//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 6230-6239 ［DOI： 10.1109/CVPR.2017.660http://dx.doi.org/10.1109/CVPR.2017.660］

Zhou X Y， Wang D Q and Krahenbuhl P. 2019a. Objects as points ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1904.07850.pdfhttps://arxiv.org/pdf/1904.07850.pdf

Zhou Y， Rui T， Li Y R and Zuo X G. 2019b. A UAV patrol system using panoramic stitching and object detection. Computers and Electrical Engineering， 80： #106473 ［DOI： 10.1016/j.compeleceng.2019.106473http://dx.doi.org/10.1016/j.compeleceng.2019.106473］

Zhu J S， Sun K， Jia S， Li Q Q， Hou X X， Lin W D， Liu B Z and Qiu G P. 2018a. Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 11（12）： 4968-4981 ［DOI： 10.1109/JSTARS.2018.2879368http://dx.doi.org/10.1109/JSTARS.2018.2879368］

Zhu J Y， Park T， Isola P and Efros A A. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 2242-2251 ［DOI： 10.1109/ICCV.2017.244http://dx.doi.org/10.1109/ICCV.2017.244］

Zhu P F， Wen L Y， Du D W， Bian X， Ling H B， Hu Q H， Nie Q Q， Cheng H， Liu C F， Liu X Y， Ma W Y， Wu H T， Wang L J， Schumann A， Brown C， Qian C， Li C Z， Li D D， Michail E， Zhang F， Ni F， Zhu F， Wang G H， Zhang H P， Deng H， Liu H， Wang H R， Qiu H Q， Qi H G， Shi H H， Li H L， Xu H Y， Lin H， Kompatsiaris I， Cheng J， Wang J Q， Yang J X， Zhou J K， Zhao J P， Joseph K J， Duan K W， Suresh K， Ke B， Wang K， Avgerinakis K， Sommer L， Zhang L， Yang L， Cheng L， Ma L， Lu L Y， Ding L， Huang M Y， Vedurupaka N K， Mamgain N， Bansal N， Acatay O， Giannakeris P， Wang Q， Zhao Q J， Huang Q M， Liu Q， Cheng Q S， Sun Q C， Laganière R， Jiang S， Wang S J， Wei S B， Wang S W， Vrochidis S， Wang S J， Lee T， Sajid U， Balasubramanian V N， Li W， Zhang W， Wu W K， Ma W C， He W R， Yang W Z， Chen X Y， Sun X， Luo X B， Lian X T， Li X F， Kuai Y L， Li Y L， Luo Y， Zhang Y F， Liu Y L， Li Y， Wang Y， Wang Y T， Wu Y W， Fan Y， Wei Y C， Zhang Y Q， Wang Z X， Wang Z Y， Xia Z Y， Xia Z Y， Cui Z， He Z W， Deng Z P， Guo Z Y and Song Z C. 2018b. VisDrone-DET2018： the vision meets drone object detection in image challenge results//Proceedings of 2018 European Conference on Computer Vision. Munich， Germany： Springer： 437-468 ［DOI： 10.1007/978-3-030-11021-5_27http://dx.doi.org/10.1007/978-3-030-11021-5_27］

Zhu X K， Yu S C， Wang X and Zhao Q. 2021. TPH-YOLOv5： improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops. Montreal， Canada： IEEE： 2778-2788 ［DOI： 10.1109/ICCVW54120.2021.00312http://dx.doi.org/10.1109/ICCVW54120.2021.00312］

Zoph B， Cubuk E D， Ghiasi G， Lin T Y， Shlens J and Le Q V. 2020. Learning data augmentation strategies for object detection//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 566-583 ［DOI： 10.1007/978-3-030-58583-9_34http://dx.doi.org/10.1007/978-3-030-58583-9_34］

Zou Z X， Chen K Y， Shi Z W， Guo Y H and Ye J P. 2019. Object detection in 20 years： a survey ［EB/OL］. ［2022-06-08］. https://arxiv.org/pdf/1905.05055.pdfhttps://arxiv.org/pdf/1905.05055.pdf

文章被引用时，请邮件提醒。

提交

鲁棒主成分分析的运动目标检测综述

智能交通系统中的车辆标志识别方法综述

“三维视觉—语言”推理技术的前沿研究与最新趋势

深度学习实时语义分割综述

融合帧间时序关系的标准胎儿四腔心超声切面自动获取