Detecting Duct Picking and Angle Cock of Train Based on Improved YOLOV5m
Due to the complexity of the operating environment and the small size of the duct pickings and angle cocks,the detection accuracy of the original YOLOV5m algorithm needs to be improved.To this end,a large-sized feature map has been added to the detection head output section of YOLOV5m to enhance the detection capability of small-sized targets.Firstly,a class residual diversion module with multiple convolutions and short connections is added to the backbone network to enrich the gradient flow information of the shallow network,and the output of the new module is used as a newly added large-sized feature map.Then,in the Neck section,the feature pyramid model FPN and path aggregation model PAN were used for multi-scale feature fusion.Finally,four different sized feature maps were obtained in the detection head section.The results show that the improved YOLOV5m has a detection frame rate of about 100 frames per second,which is 1.3%higher than the original YOLOV5m in terms of average accuracy mAP.This indicates that the improved YOLOV5m can be used as a visible light assisted model to enhance the performance of the multi-modal recognition system for train uncoupling robots.
deep learningobject detectionduct picking of trainangle cockmulti-feature
万方数据
维普
