Railway rutting defects detection based on improved RT-DETR

Railway turnouts are critical components of the rail track system, and their defects can lead to severe safety incidents and significant property damage. The irregular distribution and varying sizes of railway-turnout defects, combined with changing environmental lighting and complex backgrounds, pose challenges for traditional detection methods, often resulting in low accuracy and poor real-time performance. To address the issue of improving the detection performance of railway-turnout defects, this study proposes a high-precision recognition model, Faster-Hilo-BiFPN-DETR (FHB-DETR), based on the RT-DETR architecture. First, we designed the Faster CGLU module based on Faster Block, which optimizes the aggregation of local and global feature information through partial convolution and gating mechanisms. This approach reduces both computational load and parameter count while enhancing feature extraction capabilities. Second, we replaced the multi-head self-attention mechanism with Hilo attention, reducing parameter count and computational load, and improving real-time performance. In terms of feature fusion, we utilized BiFPN instead of CCFF to better capture subtle defect features and optimized the weighting of feature maps through a weighted mechanism. Experimental results show that compared to RT-DETR, FHB-DETR improved mAP50 by 3.5%, reduced parameter count by 25%, and decreased computational complexity by 6%, while maintaining a high frame rate, meeting real-time performance requirements.