RMSDNet: A Lightweight Object Detection Network for Rail Surface Defect
Yuejian Chen, Yan Li, Zhimin Ying, Zhipeng Wang, Mingjiang Xie
Abstract
The surface condition of rails is important for ensuring the safe and stable operation of railway vehicles, so real-time defect detection of rail surfaces is essential. However, manual inspection and mainstream non-destructive surface detection methods are not only difficult to meet the accuracy requirements but are also inefficient. To solve this problem, we propose a new rail surface defect detection method, namely, reversible multi-scale detection networks (RMSDNet) based on the improved YOLOv8-n, which can detect rail surface defects more accurately and quickly with fewer parameters and greater efficiency. First, the backbone is reconstructed using the concept of reversible column networks (RevCol) to complete feature extraction more efficiently. Secondly, the multi-section block with attention and pooling (MSAP) module is designed to enhance attention to defects and reduce noise interference during feature fusion. In addition, ghost convolution with shuffle (GSConv) is introduced to reduce the computational complexity in the process of down-sampling and further optimize the information interaction. Finally, a semi-decoupled head (SD-Head) is designed to reduce the information redundancy while ensuring detection accuracy. Experiments on the rail surface defect dataset show that our model achieves the highest [email protected] of 78.0% with the fewest parameters and lowest FLOPs compared to other mainstream object detection models.