Nondestructive Detection of Road Defects Using YOLOv9 Neural Network and Transfer Learning

Abstract— In road infrastructure monitoring, the demand for automated pavement damage detection is growing, but traditional methods relying on manual inspection or expensive equipment struggle with large-scale, real-time detection. Deep learning-based object detection offers an efficient solution, yet challenges remain in computational constraints, environmental variations, and diverse damage types. An ideal model must balance accuracy and efficiency for deployment in embedded devices, drones, and edge computing. Compared to two-stage models like Faster R-CNN, YOLO series models, particularly YOLOv9, optimize performance with PGI, GELAN structures, and reversible functions, making them suitable for constrained environments. While YOLOv9 has higher computational overhead than YOLOv8, its superior detection accuracy enhances its potential in resource-limited settings. To improve adaptability, we integrate transfer learning and semisupervised learning, reducing training complexity and enhancing generalization. Our improved YOLOv9-based method achieves efficient, high-precision detection with low computational costs. Experiments on the China Motorcycle and Japan datasets show that the YOLOv9s-TLS model improves mAP50 by 0.5\(\%\) and \(F\)1-score by 1.4\(\%\), validating the effectiveness of transfer learning in cross-environment detection.