Comparison and Optimization of Rail Defect Detection Methods Based on Object Detection Model

Rail maintenance is an extremely complex but important component of a metro system. In response to the frequent occurrence of rail defects, the rail defect detection based on object detection is compared and optimized. Firstly, the detection performance of the Single Shot MultiBox Detector (SSD) model and the Fast Region-based Convolutional Network (Faster R-CNN) model on rail defect are compared through model training and validation. Secondly, to address the issue of low defect visibility in images, the rail defect detection method combining the grayscale histogram equalization and the Faster R-CNN model is proposed. Finally, to address the issue of failed detections in rail defect detection, the Faster R-CNN model is improved by adding the Feature Pyramid Network (FPN) structure. Results show that the rail defect detection method using the Faster R-CNN model is more accurate, with the rail defects of mean average precision (mAP) is 75%. By performing grayscale histogram equalization, resulting in a 2-percentage point increase in the mAP0.75 of the rail defect detection based on the Faster R-CNN model. By combining the FPN with Faster R-CNN model, the mAP of small-scale defect detection is increased by 15 percentage points, and the AP of various rail defects is increased by 3, 13, 13 and 15 percentage points respectively.