A Multi-Category Defect Detection Model for Rail Fastener Based on Optimized YOLOv8n - Mei Chen; Maolin Zhang; Jun Peng; Jiabin Huang; Haitao Li (Machines)
Currently, object detection-based rail fastener defect detection methods still face challenges such as limited detection categories, insufficient accuracy, and high computational complexity. To this end, the YOLOv8n-FDD, an advanced multi-category fastener defect detection model designed upon the YOLOv8n with comprehensive optimizations is developed in this paper. Concretely, by introducing the CUT-based style transfer model to generate diverse defect samples, the concern due to imbalanced distribution of sample categories is effectively alleviated. The CA mechanism is incorporated to enhance the feature extraction capability, and the bounding box loss function is further upgraded to improve the model’s generalization performance. With respect to efficiency, the Conv and c2f modules of the YOLOv8n model are, respectively, replaced with the GSConv and VoVGSPCP modules, accordingly achieving a lightweight design. Comparative experimental results demonstrate that the presented YOLOv8n-FDD model outperforms several classic object detection models in terms of detection accuracy, detection speed, model size, and computational complexity.