光学精密工程2018，Vol.26Issue(12)：3040-3050,11.DOI:10.3788/OPE.20182612.3040
基于改进深度卷积网络的铁路入侵行人分类算法
Pedestrian intruding railway clearance classification algorithm based on improved deep convolutional network
摘要
Abstract
Objects intruding railway clearance pose great threat to normal railway operations.Identifying intruding pedestrians within the railway clearance limit was of great significance to ensure the safety of railway operations.The existing railway intrusion detection system only detected the intrusion, but did not distinguish whether it was a true alarm of pedestrian intrusion or false alarm caused by light interferences.To reduce false alarms, a training and test set of the alarm image samples were established.A pedestrian classification algorithm based on improved deep convolutional network, trained with combined features of HOG and high-level Alex was then proposed.First, an improved AlexNet deep convolutional neural network model was introduced to extract high-level Alex featuresby automatic learning;the extracted features were then combined with HOG features to form the combined features of Alex-HOG.Finally, the combined features were used to train the classification network.Experiments on the test set show that the proposed method has a high recognition accuracy of 98.46％in 3.78 sfor 1 498 test image samples.The improvements in both accuracy and real-time performance will greatly reduce the false alarm rate of the railway intrusion detection system.关键词
铁路异物分类识别/行人检测/深度卷积网络/HOG组合特征Key words
railway objects classification and identification/pedestrian detection/deep convolutional network/HOG combined features分类
信息技术与安全科学引用本文复制引用
郭保青,王宁..基于改进深度卷积网络的铁路入侵行人分类算法[J].光学精密工程,2018,26(12):3040-3050,11.基金项目
国家重点研发计划资助项目(No.2016YFB1200402) （No.2016YFB1200402）
铁路总公司重点研发计划资助项目(No.2017T001-B) （No.2017T001-B）
国家留学基金委员会资助项目(No.201707095075) （No.201707095075）
