Steel Surface Defect Detection Based on YOLOv9 with Denoising Diffusion Implicit Models

Due to the complexity of steel processing environments, surface defects inevitably occur during production. Detecting these defects is critical for ensuring product quality and industrial safety. Traditional manual inspection methods suffer from inefficiency and subjectivity, while existing algorithms struggle with feature extraction in complex scenarios. We propose a novel steel surface defect detection model YOLODDIM-DWConv-C3 based on YOLOv9, which enhances feature extraction capabilities while significantly reducing computational complexity. To address the scarcity of original data, we employ the Denoising Diffusion Implicit Model DDIM for data augmentation. The proposed YOLO based defect detection model minimizes computational demands, enabling seamless deployment on edge devices for real-time defect monitoring. Experimental results on the NEU-DET dataset demonstrate that YOLO-DDC outperforms existing methods in both detection accuracy and computational efficiency.We have published the complete project at https: github.com zhzhzsword YOLO-DDC.