重庆工商大学学报（自然科学版）2025，Vol.42Issue(4)：44-52,9.DOI:10.16055/j.issn.1672-058X.2025.0004.006
YOLOv8-SSDW:基于YOLOv8的带钢表面缺陷检测算法
YOLOv8-SSDW:A Steel Surface Defect Detection Algorithm Based on YOLOv8
摘要
Abstract
Objective In response to the issues of low detection accuracy,missed detections,and false alarms in existing steel surface defect detection methods,an improved defect detection algorithm,YOLOv8-SSDW,based on YOLOv8,was proposed.Methods This algorithm took YOLOv8n as the benchmark model and introduced the SKNet(Selective Kernel Networks)attention module into the backbone network structure to enhance the feature extraction and adaptability of the backbone network,allowing the network to pay more attention to defect targets during the feature extraction process.At the same time,the Slim-Neck structure was used in the neck of YOLOv8 to reduce the number of model parameters and computational load.To further enhance the network's feature extraction capability,a deformable convolution fusion method was proposed to strengthen the feature learning for defect targets.Considering the imbalance in defect sample quality,the WIoU(wise intersection over union)loss function was used,which effectively addressed the issue through its gradient gain allocation strategy,enhancing model convergence speed and regression accuracy.Results Experiments on the steel dataset showed that the average accuracy of the improved model reached 85.5％,which was an increase of 2.7％over the benchmark model.Conclusion Extensive experiments demonstrate the effectiveness of the improved network,which resolves the issue of low accuracy in steel strip surface defect detection,reduces missed and false detections,and meets real-time requirements.Compared with current mainstream models,the proposed model has certain advantages in detection accuracy and offers a valuable reference for practical detection in future research.关键词
YOLOv8/注意力机制/可变形卷积/WIoUKey words
YOLOv8/attention mechanism/deformable convolution/WIoU分类
信息技术与安全科学引用本文复制引用
戴林华,黎远松,石睿..YOLOv8-SSDW:基于YOLOv8的带钢表面缺陷检测算法[J].重庆工商大学学报（自然科学版）,2025,42(4):44-52,9.基金项目
国家自然科学基金资助项目(42074218). （42074218）
