深度学习应用于遮挡目标检测算法综述

Review of Deep Learning Applied to Occluded Object Detection

遮挡目标检测长期以来是计算机视觉中的一个难点和研究热点。目前的深度学习基于卷积神经网络,将目标检测任务作为分类任务和回归任务来处理。当目标被遮挡时,遮挡物会混淆目标之间的特征,使得深度网络不能很好地识别和推理,降低检测器在理想场景下的性能。考虑到遮挡在现实中的普遍性,对遮挡目标的有效检测具有重要研究价值。为了进一步促进遮挡目标检测的发展,对基于深度学习的遮挡目标检测算法进行了全面总结,并对已有的遮挡检测算法进行归类、分析、比较。在对目标检测进行简单概述基础上,首先,对遮挡目标检测的相关背景、研究的难点以及遮挡数据集进行了介绍;然后,对遮挡检测优化算法主要按照目标结构、损失函数、非极大值抑制以及部分语义四方面进行归纳分析,在对各种算法之间的联系以及发展脉络进行阐述后,对各种算法性能进行了比较;最后,指出了遮挡目标检测仍面临的困难,并对遮挡目标检测未来的发展方向进行了展望。

Occluded object detection has long been a difficulty and hot topic in the field of computer vision.Based on convolutional neural network,the deep learning takes the object detection task as a classification and regression task to handle,and obtains remarkable achievements.The mask confuses the features of object when the object is occluded,making the deep convolutional neural network cannot handle it well and reducing the performance of detector in ideal scenes.Considering the universality of occlusion in reality,the effective detection of occluded object has important research value.In order to further promote the development of occluded object detection,this paper makes a comprehensive summary of occluded object detection algorithms,and makes a reasonable classification and analysis.First of all,based on a simple overview of object detection,this paper introduces the relevant theoretic background,research difficulties and datasets about occluded object detection.After,this paper focuses on the algorithms to improve the performance of occluded object detection from the aspects of object structure,loss function,non-maximum suppression and semantic partial.This paper compares the performance of different detection algorithms after summarizing the relationship and development of various algorithms.Finally,this paper points out the difficulties of occluded object detection and looks forward to its future development directions.