Research on real-time detection method of rail corrugation based on improved ShuffleNet V2

Rail corrugation is a common wear mechanism of high-speed railways and subways, which can cause derailment and running noise. However, rail corrugation only has slight texture change on the rail surface, so it is difficult to detect accurately by traditional detection methods. In this paper, a real-time detection method of rail corrugation based on machine vision and a convolutional neural network is proposed, which effectively improves the accuracy and efficiency of rail corrugation detection. Combined with the gray features of each part of the image, a rail surface segmentation method based on the gray maximum value of the sliding window is also proposed. Moreover, the obtained rail surface image is clearer and the feature information of the rail surface can be completely retained, compared with the adaptive threshold segmentation and edge detection segmentation. ShuffleNet V2, a lightweight convolutional neural network, was selected as the corrugation detection model. The squeeze-and-excitation module was integrated into its basic unit to improve its channel attention, and the activation function was re-selected to make the detection have better real-time performance and accuracy. Through experimental verification, the average detection time of a single image of the improved model is 4.01ms, and the detection accuracy is 2.78% higher than that of the unimproved ShuffleNet V2. The research results will be beneficial to the development of the intelligent real-time detection of rail corrugation.

Automated summary

This paper proposes a real-time detection method for rail corrugation based on machine vision and a convolutional neural network, which improves the accuracy and efficiency of rail corrugation detection. A rail surface segmentation method based on the gray maximum value of the sliding window is also proposed. The experimental results show that the improved model has an average detection time of 4.01ms and a detection accuracy 2.78% higher than the unimproved ShuffleNet V2. These research results will contribute to the development of intelligent real-time detection of rail corrugation.