YOLO-DD: Improved YOLOv5 for Defect Detection

As computer technology continues to advance, factories have increasingly higher demands for detecting defects. However, detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes. To address this issue, this paper proposes YOLO-DD, a defect detection model based on YOLOv5 that is effective and robust. To improve the feature extraction process and better capture global information, the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer (RDAT). Additionally, an Information Gap Filling Strategy (IGFS) is proposed to improve the fusion of features at different scales. The classic lightweight attention mechanism Squeeze-and-Excitation (SE) module is also incorporated into the neck section to enhance feature expression and improve the model’s performance. Experimental results on the NEU-DET dataset demonstrate that YOLO-DD achieves competitive results compared to state-of-the-art methods, with a 2.0% increase in accuracy compared to the original YOLOv5, achieving 82.41% accuracy and 38.25 FPS (frames per second). The model is also tested on a self-constructed fabric defect dataset, and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5, demonstrating its stability and generalization ability. The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.