Track Defect Detection Based on Improved Rtmdet

The railway track, as a fundamental infrastructure for rail transport, directly impacts the safety of train operations. Track defects such as cracks and bolt loosening, if not detected and repaired in time, can lead to severe safety accidents. In recent years, deep learning-based track defect detection algorithms have achieved good results. However, there are still some issues that need further research, such as low detection accuracy in complex track scenarios and limited defect detection capability for diverse railway components. To address these problems, this paper proposes an improved algorithm based on RTMDet. Firstly, an enhanced PSA polarized self-attention mechanism and Normlincomb activation function are added to the feature extraction module of RTMDet to enhance the model's feature representation capability. Secondly, a custom CSR-PAFPN feature fusion module is introduced to more effectively combine information of different scales, improving the model's performance in detecting various defects. Additionally, a detection head for small-sized track defects is added, utilizing high-resolution feature maps for more accurate detection of minute defects. Finally, a custom dataset for track defect detection is constructed, and the proposed method is tested on this dataset. Experimental results show that on the custom dataset, the improved algorithm achieved mAP of 66%, which is an increase of 3.5% over the original RTMDet algorithm and also higher than other outstanding object detection algorithms.