RMSDNet: A Lightweight Object Detection Network for Rail Surface Defect

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 nondestructive surface detection methods are not only difficult to meet the accuracy requirements but also inefficient. To solve this problem, we propose a new rail surface defect detection method, namely, reversible multiscale detection networks (RMSDNets) 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. Second, the multisection 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 downsampling 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 mAP@0.5 of 78.0% with the fewest parameters and lowest floating point operations (FLOPs) compared to other mainstream object detection models.