Steel Surface Defect Detection Method Based on Improved YOLOv8

Abstract

This research aims to improve the accuracy and efficiency of steel surface defect detection and address the current insufficient algorithm performance in steel defect detection, especially the challenges in multi-scale feature extraction and parameter efficiency. Based on the improved YOLOv8 network structure, three key improvement points are introduced: the DBB module optimizes multi-scale feature extraction, the MSCA attention mechanism enhances the model's defect detection accuracy, and the optimized GDetect module reduces the amount of parameters and calculations. Extensive experiments and comparative analysis using the NEU-DET data set verify the effectiveness of the improved method. Ablation experiments show that using DBB or MSCA alone has limited performance improvement, but when MSCA and GDetect are combined, the model has significant improvements in Precision, Recall, and mAP50 indicators. Comparative experiments show that the performance of this algorithm is close to or even surpasses some more complex models while remaining relatively lightweight. The average accuracy mean analysis for six defect categories also shows the superior performance of this algorithm for various types of defects. The steel surface defect detection method proposed in this study based on the improved YOLOv8 network has made significant progress in the field of steel defect detection. The improved method improves detection accuracy, solves common problems, and achieves high performance while maintaining lightweight, providing an efficient and feasible solution for steel surface defect detection.