Railway fastener condition detection method based on improved YOLOv9

SHA Bonan; BAI Tangbo; XU Guiyang; JIA Haopeng

doi:10.11860/j.issn.1673-0291.20240135

Abstract：To address the prevalent false positives and missed detections of railway fasteners in com-plex scenarios such as switch machines and turnouts for railway fastener inspection tasks,this paper proposes a railway fastener condition detection method based on an improved YOLOv9.First,to over-come the challenge of extracting features from fastener regions in complex scenes,the Large Selective Kernel(LSK)attention mechanism is integrated with the RepNCSPELAN4 module.This optimiza-tion enhances the performance of the feature extraction module,enabling more effective capture of critical feature information in fastener regions and improving the model's adaptability to diverse scenes and targets.Second,to better distinguish subtle details of confusing fastener damage states,a feature fusion network based on Space to Depth(SPD)convolution is developed,thereby increasing accuracy in low-resolution and small-object detection and ensuring precise identification of fastener damage states even against complex backgrounds.Third,Shape IoU is adopted as the new loss function to more accurately measure the overlap between predicted and ground-truth bounding boxes,endowing the model with greater robustness and superior target localization precision.Finally,to validate the ef-fectiveness of the proposed method,a real-world railway fastener dataset encompassing complex oper-ating conditions is collected and constructed,and comprehensive comparative experiments are con-ducted.Experimental results demonstrate that the proposed method effectively detects railway fastener conditions,achieving a 1.3%improvement in detection accuracy over the baseline model,while reduc-ing the false detection rate by 0.7%and the missed detection rate by 1.4%.This enhances the reliabil-ity and stability of railway fastener condition detection in complex scenarios.