A Deep Convolutional Neural Network for Detection of Rail Surface Defect

Railway surface defect detection is an important technical measure to ensure the safe operation of the rail transit system. Due to the complex and diverse features of rail surface defects and the uneven curvature of the image caused by the track surface, it is difficult to obtain better detection results by traditional machine vision technology. The existing deep learning-based method only classifies the defect image and cannot locate the defect location. At the same time, the network structure of this method is complex, and the detection accuracy and speed cannot be well balanced. This paper proposes a new method for detecting railway surface defects based on end-to-end target detection and lightweight convolutional network structure. This method uses MobileNetV2 as the backbone network to extract image features, and then performs multi-scale defect detection to ensure real-time. Under the premise of detection, higher detection accuracy can be obtained.