Research on the improved railway foreign body intrusion limit detection algorithm based on YOLOv5s
Therefore,this paper proposes an improved railway foreign object intrusion detection algorithm NH-YOLOv5 based on YOLOv5s.Because there is no public railway invasion data set,this article uses a camera installed on the locomotive to perform real time cameras,and collects foreign objects invading images on the spot to establish an railway intrusion data set RD.Use the established data set RD to evaluate the NH-YOLOv5 model.The experimental results show that the accuracy and memories of the NH-YOLOv5 algorithm proposed are 92.6％and 78.3％,respectively,with the average average accuracy of mAP@0.5 and mAP@0.5∶0.95,respectively 85.3％and 61.5％,respectively,respectively,respectively,respectively.61.5％.Compared with the original YOLOv5s model,the precision is increased by 1.4 percentage points,the recall rate is increased by 7.4 percentage points,and the average average precision is increased by mAP@0.5 and mAP@0.5∶0.95,respectively,and the average accuracy is increased by 6.3 and 7.7 percent points.The results show that the proposed algorithm has a better detection effect than the YOLOv5s algorithm.This algorithm effectively improves the problems of missions detection and erro detection,and improves the detection ability of small targets,which verifies the accuracy and usability of the method.
NETL
NSTL
万方数据
维普
