Automated detection of railway defective fasteners based on YOLOv8-FAM and synthetic data using style transfer

Fastener damage detection is an integral component of track safety inspections. The lack of balanced dataset caused by insufficient data on defective fasteners poses a significant challenge to the current development of robust fastener detection models. This study proposes the YOLOv8-FAM detection model algorithm, which combines the enhanced capabilities of YOLOv8, and generates realistic images of defective fasteners by performing style transfer on masked images of non-defective fasteners, thereby creating a balanced dataset for training the detection model. Experimental results show that the defect detection accuracy of YOLOv8-FAM improves by 8% compared to the original model, while also reducing the data acquisition cost by 40%. The YOLOv8-FAM model surpasses existing models in detecting defective fasteners while minimizing inference costs to the maximum extent. The proposed style transfer data synthesis method drives the practical deployment of deep learning, offering an efficient and cost-effective solution for the transportation infrastructure industry.