面向目标检测的视点规划方法综述  

View planning methods of object detection:a survey

作　　者：王健宇 朱枫[2,3] 郝颖明[2,3] 王群 赵鹏飞[2,3,4] 孙海波 Wang Jianyu;Zhu Feng;Hao Yingming;Wang Qun;Zhao Pengfei;Sun Haibo(Faculty of Robot Science and Engineering,Northeastern University,Shenyang 110169,China;Key Laboratory of Opto-Electronic Information Processing,Chinese Academy of Sciences,Shenyang 110016,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai 200050,China)

机构地区：[1]东北大学机器人科学与工程学院,沈阳110169 [2]中国科学院光电信息处理重点实验室,沈阳110016 [3]中国科学院沈阳自动化研究所,沈阳110016 [4]中国科学院大学,北京100049 [5]中国科学院上海微系统与信息技术研究所,上海200050

出　　处：《中国图象图形学报》2025年第3期641-659,共19页Journal of Image and Graphics

摘　　要：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 pixelbased 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 re

关 键 词：目标检测 主动视觉 参数调整 视点规划 智能感知 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]