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.
Li et al. (2026) studied this question.