A Rail Surface Defect Detection Method Based on Pyramid Feature and Lightweight Convolutional Neural Network

Abstract

The application of computer vision technology in defect detection of industrial products is a popular research direction in recent years. This article presents the pyramid feature convolutional neural network (CNN) for defect detection of rail surfaces. First, multi-scale feature maps are extracted based on the characteristics of defects and backgrounds by the pyramid feature extraction module (PFEM). Then the feature maps are input to a lightweight network consisting of a small number of parameters. The network is trained with only 40% data of the dataset using binary cross-entropy loss function and the intersection of union (IOU) loss function. In the experiment, the performance of the proposed method is evaluated using the rail surface defect dataset (RSDD) dataset by comparing it with other methods. The experimental results show that the segmentation performance and real-time performance of the proposed method are better than those of other methods.