Research on Image Detection Algorithm for Failure of Key Parts of Railway Freight Wagon Based on Deep Learning

Abstract

The parts failure of railway freight wagon is one of the main causes of wagon accidents. Currently, train inspection work mostly uses "human eye recognition" to determine the part failures, which cannot realize the automatic fault identification. For the problem of automatic identification of key parts failure, an image detection algorithm based on is proposed based on the combination of deep learning and image processing technology according to the idea of "first locate, then identify". First, the parts detection data set and parts fault identification data set are constructed. Then, the Small-Target-Detect-Layer and CBAM YOLOv5s (SC-YOLOv5s) are integrated with the small target detection layer and Convolutional Block Attention Module to realize the localization, classification and cropping of multi-scale key parts. Finally, an algorithm based on object detection results and prior knowledge is proposed to directly determine the type of loss fault, and the improved MobileNetV3 classification algorithm is proposed to realize the automatic identification of three kinds of faults: oil dumping of rolling bearing, loosening of locking plate, and breakage of rocking pillow spring, as well as the image processing method to realize the automatic identification of two kinds of faults: bending of crossover rod and breakage of front cover of rolling bearing. The results show that SC-YOLOv5s mAP@0. 5 and mAP@0. 5: 0. 95 can reach 99. 3% and 74. 9% and the detection speed can reach 36. 09 FPS; the improved MobileNetV3 algorithm can reach 98. 63%, 99. 34%, and 90. 21% of the recognition accuracy of bearing oil dumping, locking plate loosening, and rocking pillow spring breakage; the recognition accuracy of the image processing method for crossbar bending and rolling bearing front cover breakage can reach 95. 32% and 82. 88%, respectively.