Year: 2025
Type: article
Abstract: Surface defects on strip steel are often inevitable due to limitations in raw materials and manufacturing processes. To improve defect detection accuracy, we propose the YOLOv5s-SDF algorithm, which integrates ShuffleAttention in the neck and DyHead in the head of the YOLOv5s framework. The novelty lies in the use of ShuffleAttention to model spatial-channel dependencies and the incorporation of Focal-CIoU loss to mitigate the influence of low-quality samples. Experiments on the NEU-DET dataset show that YOLOv5s-SDF achieves 69.37% Precision, 74.21% Recall, 76.32% [Formula: see text], and 38.60% [Formula: see text], outperforming the baseline YOLOv5s by 0.2%, 1.72%, 3.57%, and 0.84%, respectively.
Source: Scientific Reports
Authors: Kun Liu, Xinbo Chang, Hongru Ma, Liang Lv, Hang Chen
Institution: Qingdao University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4416694960