Lightweight Algorithm for Rail Fastener Status Detection Based on YOLOv8n

Abstract

To improve the accuracy of rail fastener detection and deploy deep learning models on mobile platforms for fast real-time inference, this paper proposes a defect detection model for rail fasteners based on an improved YOLOv8n. Considering the significant aspect ratio differences of rail fasteners, we designed the EIOU+ as the regression box loss function. The model is compressed and trained using an improved channel-wise knowledge distillation (CWD+) approach to address the challenge of accurately recognizing minor defects in rail fasteners. We introduced a feature extraction module to design a feature extraction network as the distillation teacher model (YOLOv8n-T) and a lightweight cross-stage partial bottleneck with two convolutions and a fusion module (C2f) to improve the YOLOv8n backbone network as the distillation student model (YOLOv8n-S). Experiments conducted on data collected from actual rail lines demonstrate that after CWD+ distillation training, the model’s mean detection accuracy (IOU = 0.5) reached 96.3%, an improvement of 2.7% over the original YOLOv8n algorithm. The recall rate increased by 4.5%, the precision by 2.7%, the number of floating-point operations decreased by 13%, and the detection frame rate frames per second (FPS) increased by 6.1 frames per second. Compared with other one-stage object detection algorithms, the CWD+ distilled model achieves the precise real-time detection of rail fastener conditions.