基于深度学习的目标检测研究综述

目标检测是计算机视觉领域内的热点课题，在机器人导航、智能视频监控及航天航空等领域都有广泛的应用.本文首先综述了目标检测的研究背景、意义及难点，接着对基于深度学习目标检测算法的两大类进行综述，即基于候选区域和基于回归算法.对于第一类算法，先介绍了基于区域的卷积神经网络（Region with Convolutional Neural Network，R-CNN）系列算法，然后从四个维度综述了研究者在R-CNN系列算法基础上所做的研究：对特征提取网络的改进研究、对感兴趣区域池化层的改进研究、对区域提取网络的改进研究、对非极大值抑制算法的改进研究.对第二类算法分为YOLO（You Only Look Once）系列、SSD（Single Shot multibox Detector）算法及其改进研究进行综述.最后根据当前目标检测算法在发展更高效合理的检测框架的趋势下，展望了目标检测算法未来在无监督和未知类别物体检测方向的研究热点.

Survey of Object Detection Based on Deep Learning

Object detection is a hot topic in the field of computer vision,and has been widely used in robot navigation,intelligent video surveillance,aerospace,and other fields.The research background,significance and challenges of object detection were introduced.Then the object detection algorithms based on deep learning were reviewed according to two categories:candidate region-based and regression-based.For the candidate region-based algorithms,we first introduced the R-CNN (Region with Convolutional Neural Network) based series of algorithms,and then the R-CNN based methods were overviewed from four dimensions:the research of feature extraction networks,the region of interesting pooling researches,improved works based on region proposal networks,and some improved approaches of non maximum suppression algorithms.Next,the regression-based algorithms were surveyed in terms of YOLO(You Only Look Once) series and SSD(Single Shot multibox Detector) series.Finally,according to the current trend of object detection algorithms that are developing more efficient and reasonable detection frameworks,the future research focuses of unsupervised and unknown category object detection directions were prospected.