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收稿日期：2024-06-18，
修回日期：2024-08-28，
纸质出版日期：2025-03-16
移动端阅览
目标检测是计算机视觉领域的基础研究方向之一。由于图像采集时物体摆放密集、光照条件差等因素导致图像失去细节，当使用此类图像作为输入时，常规的目标检测算法对目标物的检测结果无法满足任务需求。为了解决这类问题，面向目标检测的视点规划这一智能感知方法应运而生，其可自主分析当前条件下影响检测任务的因素，调整相机的位姿参数规避影响，实现目标物准确检测。面向目标检测的视点规划方法不仅可以辅助计算机视觉的其他领域，也会为未来的智能化生活提供便利。为了反映其研究现状和最新进展，本文梳理了2007年以来的文献，对国内外的研究方法做出概括性总结。首先，以算法应用的场景维度和调整参数作为分类依据，将面向目标检测的视点规划方法分为二维像素调整的规划方法、三维空间移动的规划方法以及两者结合的规划方法3类，本文重点对前两类方法进行分析与总结。其次，解析每类方法的基本思想，并指出各类方法需解决的关键问题，然后对解决问题的主要研究方法进行归纳和分析，并总结各自的优点和局限性。除此之外，本文也对各类场景下可使用的数据集和评价指标进行简要介绍。最后，在目前方法的分析基础上，探讨面向目标检测的视点规划领域所面临的挑战，并对未来研究方法进行展望。
Object detection is one of the fundamental research directions in the field of computer vision and is also the cornerstone of advanced vision research. When objects are densely arranged or located under poor lighting conditions， crucial details can be lost during image acquisition. When using images with missing details as input， the detection results from conventional target detection algorithms often fail to meet task requirements. To address these challenges， intelligent perceptual methods for point-of-view planning in target detection have emerged. These methods can autonomously analyze the factors affecting detection tasks under current conditions， adjust the camera’s pose parameters to mitigate these effects， and achieve accurate target detection. This paper reviews and analyzes relevant studies since 2007 and summarizes domestic and foreign research methods to reflect the research status and the latest development of viewpoint planning methods for object detection. For simplification， this method is called active object detection （AOD） in this article. According to the different use scenarios， this paper divides the active object detection methods into two categories： AOD in two-dimensional scenes， AOD in three-dimensional scenes， and AOD combining the two. The third method is uncommon； thus， this paper mainly introduces the first two methods. Specifically， in two-dimensional scenes， AOD methods are divided into pixel-based methods and those that simulate camera parameters， depending on whether a single-pixel or an overall image is being planned. The most important part of the pixel-based approach is the selection of the target pixel point and the strategy for planning the next pixel. Typically， integral features， scale features， or key points， which are the parts of the target that have the largest gap between the target and the background， are used by researchers to locate the possible location of target pixels. After positioning the target pixel， the moving position of the next pixel will be set in accordance with the category of the region to ensure the continuity of the front and back frames and avoid the task failure caused by planning errors. For AOD methods that simulate camera parameters， different influencing factors cause various difficulties in target detection. Therefore， researchers have designed different planning scenarios by analyzing the types of influencing factors， and some excellent results have emerged in recent years. As time goes by， the popularity of moveable robots has introduced AOD into a new development environment： 3D scenes. In three-dimensional environments， the AOD method enables the intelligent agent to actively select the next viewpoint pose in space， thereby mitigating the influence of interference factors on the target detection process. We classify 3D scenes based on the degree of known spatial location information into two categories： 3D scenes with known spatial relationships and 3D scenes with unknown spatial relationships. In the first type of scenario， the placement of the target object and surrounding objects， the display of spatial category markers， and the range of viewpoint planning are all known， and the AOD method can perform viewpoint planning based on the known information. In this type of approach， researchers focus more on the representation of relationships and the selection of the next viewpoint in a fixed search space. The second type of space has no information to assist， and the agent can only rely on the observation results to select the next viewpoint. In real life， situations where relationships are unknown are highly common； therefore， the design of AOD methods in this situation is currently a popular research direction. Researchers have made considerable efforts to provide detailed descriptions due to the close relationship between the planning strategy of such scenarios and the observed results. In AOD， observation information is usually referred to as state expression， and a detailed expression leads to improved strategy generation. In addition， researchers have made numerous efforts in the evaluation function of the next view to evaluate the next viewpoint and modify the planning strategy. AOD has two main objectives in unknown environments： path optimization and detection effect optimization. The evaluation function is generally divided into single-element and multi-element evaluations based on the types of evaluation factors. Despite the accuracy of multi-element evaluation， the selection of elements in different problems must be highly consistent. Identifying the same components across various scenarios to design a universal evaluation function remains a potential breakthrough area for researchers in the future. In addition to analyzing the methods mentioned above， this paper also provides a brief introduction to the datasets that AOD methods can use in different types of scenarios. The viewpoint planning in two-dimensional scenes is consistent with the scenes used by conventional object detection methods. Therefore， the datasets， such as large-scale public datasets COCO and Pascal VOC， have numerous overlaps. Meanwhile， the evaluation indicators of the two methods are also the same； therefore， performance comparison can be directly conducted. Considering motion factors， directly comparing detection results on 3D datasets such as AVD and T-LESS to determine the accuracy of the movement path is impossible. Therefore， researchers have designed task success rate （SR） and average travel distance as the leading indicators to measure the effectiveness of the AOD algorithm. Notably， although many excellent results have been achieved in viewpoint planning methods oriented toward target detection， some parts can still be improved in terms of scene design and research methodology. First， some real physical elements can be added to the scene design to transform the planning problem into an optimization problem under certain constraints. Second， the methods suitable for two- and three-dimensional scenes are closely combined， further realizing accurate detection by changing the sensor parameters in inaccessible areas. Finally， detection-oriented viewpoint planning methods typically output discrete actions and are also tightly bound to the task. Therefore， viewpoint planning in continuous environments or establishing a generic framework for task-independent viewpoint planning can also be considered future directions.
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