Optimizing surface defect detection with YOLOv9: the role of advanced backbone models

YOLO algorithmic models are widely utilized for detecting surface defects, offering a robust and efficient approach to identifying various flaws and imperfections on material surfaces.

In this study, we explore the integration of six distinct backbone networks within the YOLOv9 framework to optimize surface defect detection in steel strips. Specifically, we improve the YOLOv9 framework by integrating six representative backbones-ResNet50, GhostNet, MobileNetV4, FasterNet, StarNet, and RepViT-and conduct a systematic evaluation on the NEU-DET dataset and the GC10-DET dataset. Using YOLOv9-C as the baseline, we compare these backbones in terms of detection accuracy, computational complexity, and model efficiency.

Results show that RepViT achieves the best overall performance with an mAP50 of 68.8%, F1-score of 0.65, and a balanced precision-recall profile, while GhostNet offers superior computational efficiency with only 41.2 M parameters and 190.2 GFLOPs. Further validation on YOLOv5-m confirms the consistency of the results.

The study offers practical guidance for backbone selection in surface defect detection tasks, highlighting the advantages of lightweight architectures for real-time industrial applications.