CASIA OpenIR  > 智能制造技术与系统研究中心
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
Conference NameChinese Control and Decision Conference
Conference Date2019-6-3
Conference Place中国江西省南昌市
Abstract

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.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23640
Collection智能制造技术与系统研究中心
Corresponding AuthorTang SM(汤淑明)
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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