PGCF-YOLO: a railway fastener detection algorithm based on improved YOLOv8n

Railway fasteners are critical components for maintaining track stability and ensuring the safe operation of trains. Accurate and real-time detection of fastener defects is essential for achieving intelligent railway maintenance. However, existing object detection algorithms often struggle to balance detection accuracy, computational efficiency. To address these challenges, this paper proposes an improved lightweight detection algorithm—PGCF-YOLO (Pyramid Split Attention - GSConv + VoV-GSCSP – CARAFE – FocalerIoU – MPDIoU – You Only Look Once), based on the YOLOv8n architecture, aimed at enhancing the overall performance of the model. First, a Pyramid Split Attention (PSA) module is integrated into the backbone to strengthen the model’s capability in perceiving complex defect features. Then, lightweight GSConv and VoV-GSCSP modules are introduced into the Neck to reduce parameter count and computational overhead while maintaining strong feature extraction capacity. The CARAFE upsampling operator is adopted to replace traditional nearest-neighbor interpolation, improving the model’s ability to capture both fine-grained details and global semantic information. Finally, a novel regression loss function, Focaler-MPDIoU, is proposed to enhance bounding box localization accuracy and accelerate training convergence. Experimental results demonstrate that PGCF-YOLO achieves excellent inference efficiency while maintaining high detection accuracy, reaching 99.3% mAP and 128 FPS, which represent improvements of 2.2% in accuracy and 12 FPS in speed over the original YOLOv8n. Furthermore, the parameter count and GFLOPs are reduced by 11.0% and 17.3%, respectively. Compared with other mainstream object detection models, PGCF-YOLO demonstrates superior performance in detection accuracy, inference speed, and model size. Experimental results on the E-Type fastener dataset demonstrate that PGCF-YOLO possesses strong generalization capability, validating its practical applicability in railway fastener defect detection tasks.