文献类型：会议论文

英文题名：Forward Vehicle Detection Based on Incremental Learning and Fast R-CNN

作者：Shi, Kaijing[1];Bao, Hong[1];Ma, Nan[2]

第一作者：Shi, Kaijing

通讯作者：Bao, H[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：13th International Conference on Computational Intelligence and Security (CIS)

会议日期：DEC 15-18, 2017

会议地点：Hong Kong, HONG KONG

语种：英文

外文关键词：deep learning; image; vehicle detection; Fast R-cnn; accurate rate

摘要：Recently the research of vehicle detection is mainly through machine learning, but it still has low detection accuracy problem. With the study of researchers, using deep learning methods of vehicle detection becomes hot. In this paper, a selective search method and a target detection model based on Fast R-CNN are used to detect vehicle. The strategy optimizes the model by preprocessing the sample image and the new network structure. Firstly, the experiment uses the public KITTI data set and self-collected BUU-T2Y data set, respectively, for training validation and test. Secondly, based on the original data set, the experiments go on through incremental learning, combining the KITTI dataset with the BUU-T2Y dataset. The experimental results show that the proposed method is superior to the result of multi -feature and classifier detection in terms of accuracy. To a large extent, the proposed method solved the problem of missing vehicle for detection and improved the accuracy of vehicle testing and robustness.