Research on underbody bolt detection method of lightweight train based on C3F-YOLOv5
It is of great significance to improve the identification accuracy and detection speed of bolt under the complex train background. In order to more efficiently detect bolts,a method for detecting bolts under lightweight train based on C3F-YOLOv5 was proposed. A self-designed crawler bottom detection robot was used to obtain the bolt pictures of the train bottom. The bolt pictures of the simulated platform were mixed with the real bolt pictures of the train bottom as the final data set. The Bottleneck structure in the C3 module was replaced with a Faster_Block structure,improving it to the C3F module. Furthermore,the C3F module was compared with the lightweight structures of FasterNet,GhostNet,and MobileNetV3. In addition,the attention mechanism CA module was introduced,and the original loss function LGIoU was replaced with the more suitable LMPDIoU. The ablation experiments were carried out,and the SE module and CBAM module were added to compare with the CA module,respectively. Finally,LAMP score was used to sort the weight parameters of the model,and the unimportant weight parameters were pruned as a new model compression method. The C3F-YOLOv5s network model compressed by the final model was compared with YOLOv4,YOLOv7,Mask R-CNN,and RetinaNet. The research results show that when using mixed datasets,the average detection accuracy and detection speed of the final network model reach 92.8％ and 256.7 FPS,respectively. Compared to the other four classic deep learning network models,the improved model exhibits superior detection performance,robustness,and generalization performance. This method can make the network more adaptable to the real situation of the train bottom after training when more pictures of the real train bottom cannot be obtained. The improved algorithm can promote the identification accuracy and detection speed of bolts,and can provide technical reference and theoretical support for subsequent related research.
万方数据
维普
