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A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving
Lu JL(卢佳琳); Tang SM(汤淑明); Wang JQ(王金桥); Zhu HB(朱海兵); Wang YK(王云宽)
2019-06-03
会议名称Chinese Control and Decision Conference
会议日期2019-6-3
会议地点中国江西省南昌市
摘要

Vehicle and pedestrian detection is significant in autonomous driving. It provides information for path planning, lane selection, pedestrian and vehicle tracking, pedestrian behavior prediction, etc. In recent years, the state-of-the-art object detection algorithms have been emerged on the base of deep convolutional neural networks, which can get higher accuracy and efficiency detection results than traditional vision detection algorithms. In this paper, we first introduce and summarize some state-of-the-date object detection algorithms based of deep convolutional neural networks and the improvement ideas of these algorithms. Their frameworks are extracted. Then, we choose several different algorithms and analyze their running results on challenging datasets, Pascal VOC and KITTI. Next, we analyze the current detection challenges as well as their solutions. Finally, we provide insights into use in autonomous driving, such as vehicle and pedestrian detection and driving control.

收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/23640
专题智能制造技术与系统研究中心
通讯作者Tang SM(汤淑明)
作者单位中国科学院自动化研究所
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Lu JL,Tang SM,Wang JQ,et al. A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving[C],2019.
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