GDM-YOLO: A Model for Steel Surface Defect Detection Based on YOLOv8s

Abstract

Steel materials are extensively used across various industries. Detecting surface defects in steel strips during production processes is crucial. Existing steel surface defect detection methods exhibit inadequate accuracy and excessive computational complexity, posing challenges for real-time industrial deployment. In this paper, a novel model is designed named GDM-YOLO, specifically tailored for steel surface defect detection tasks, built upon the YOLOv8s network. Firstly, the Space-to-Depth Ghost Convolution (SPDG) downsampling module is introduced and used in the backbone network, aimed at minimizing information loss during downsampling operations while optimizing computational efficiency. Secondly, this work introduces the C2f-Dilated-Reparam-Block (C2f-DRB) module, leveraging reparameterization and large kernel convolutions to enhance feature extraction capabilities without compromising inference costs. Lastly, the novel Multiscale Feature Enhancement Block (MFEB) module was designed, to enhance the small target detection layer by integrating multi-scale feature fusion, further improving detection accuracy. Experimental results demonstrate a 3% improvement in detection accuracy on the NEU-DET dataset compared to the baseline YOLOv8s model. Our approach achieves superior detection performance while reducing parameter requirements and computational complexity, meeting the real-time demands of steel surface defect detection in industrial production.

Keywords

• Reparameterization
• surface defect detection
• steel strips
• YOLO