Abstract
The detection of rail surface defects is an important tool to ensure the safe operation of rail transit. Due to the complex diversity of track surface defect features and the small size of the defect area, it is difficult to obtain satisfying detection results by traditional machine vision methods. The existing deep learning-based methods have the problems of large model sizes, excessive parameters, low accuracy and slow speed. Therefore, this paper proposes a new method based on an improved YOLOv4 (You Only Look Once, YOLO) for railway surface defect detection. In this method, MobileNetv3 is used as the backbone network of YOLOv4 to extract image features, and at the same time, deep separable convolution is applied on the PANet layer in YOLOv4, which realizes the lightweight network and real-time detection of the railway surface. The test results show that, compared with YOLOv4, the study can reduce the amount of the parameters by 78.04%, speed up the detection by 10.36 frames per second and decrease the model volume by 78%. Compared with other methods, the proposed method can achieve a higher detection accuracy, making it suitable for the fast and accurate detection of railway surface defects.
Highlights
Fengshou Gu and Xihui (Larry) LiangWith the prosperous development of the railway industry, the mileage, speed and density of operations continue to increase, and the inspection requirements for railways are further improved [1]
In order to solve the above problems, this paper proposes an improved YOLOv4 [24]
The rapid, accurate and intelligent detection of rail surface defects is of great significance for ensuring the safe operations of railway vehicles
Summary
With the prosperous development of the railway industry, the mileage, speed and density of operations continue to increase, and the inspection requirements for railways are further improved [1]. Depthwise separable convolution is applied for the PANet layer in YOLOv4 to further reduce the amounts of the parameters It treats rail surface defect detection as an end-to-end regression problem and ensures the effectiveness of rail surface defect detection with a simplified network, improving the detection speed and accuracy. The main contributions of this paper are as follows: (1) The MobileNetV3 network is proposed to optimize the YOLOv4 model for rail surface defect detection, using depthwise separable convolution for the PANet layer in YOLOv4. This method optimizes the parameter quantity and model size and improves the detection speed.
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