Modeling and Prediction on Defect Detection of Steel Surface by Using Modified YOLO8

In this study, we focus on surface defects in steel and conduct an analysis of various detection methods for steel surface defects. The detection of steel surface defects is a crucial analysis that ensures the quality of steel production. To address challenges such as low detection accuracy and inadequate feature extraction capability in steel surface defect detection, an enhanced YOLOv8-based steel defect detection algorithm, GS-YOLO, is proposed and implemented for the stated analysis. The network neck is augmented with an information aggregation-distribution mechanism module (GD) to strengthen cross-scale information recognition for steel surface defects. Furthermore, a scale sequence feature fusion module (ScalSeq) is employed to capture both high-dimensional and low-dimensional detail information from feature maps, enabling a more comprehensive integration of multi-scale features and enhancing the model’s performance in addressing multi-scale challenges. In the context of NEU-DET, several experiments have been conducted within the YOLO environment. The results of these experiments indicate that the modified/improved GS-YOLO model has reached an accuracy of 76.6%. This is a 2.5% improvement over the original method, and in general, it outperforms other standard object identification models.