A lightweight algorithm for steel surface defect detection using improved YOLOv8.

ABSTRACT: In response to the issues of low precision, a large number of parameters and high model complexity in steel surface defect detection, a lightweight algorithm using improved YOLOv8 is proposed. Firstly, GhostNet is utilized as the backbone network in order to reduce the number of model parameters and computational complexity. Secondly, the MPCA (MultiPath Coordinate Attention) attention mechanism is integrated to enhance feature extraction capabilities. Finally, the SIoU (Simplified IoU ) is used to replace the traditional CIoU loss function, which can make the anchor frame more fast and accurate in the regression process, to improve the stability and the robustness of detection. The experimental results indicate that these enhancements have led to a reduction of 37% in calculation amount f

SUBMITTER: Ma S 

PROVIDER: S-EPMC11910532 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature