机电工程技术2026，Vol.55Issue(2)：78-83,6.DOI:10.3969/j.issn.1009-9492.2025.00047
基于改进FasterNet-YOLOv8的焊缝表面缺陷检测算法
Weld Surface Defect Detection Algorithm Based on Improved FasterNet-YOLOv8
摘要
Abstract
Aiming at the problem that weld defects have strong interference from complex backgrounds,and their detection accuracy and efficiency are low,an improved FasterNet-YOLOv8 defect detection algorithm is proposed.The FasterNet lightweight model backbone is replaced on the Backbone side to capture important feature information.FasterNet-Block and convolution and attention fusion module(CAFM)are developed into the feature extraction module of the network,and a novel C2f-Faster-CAFM lightweight architecture is designed to reduce the redundant channels of the network while adaptively capturing global key information.The feature focused diffusion pyramid network(FDPN)is designed to enhance the multi-scale information fusion extraction capability,thereby improving the robustness and detection accuracy of the network in multi-scale scenes.Experimental results show that compared with the original YOLOv8 algorithm,the precision of FasterNet-YOLOv8 reaches 94.9%,the recall reaches 93.5%,and the average detection accuracy is increased to 97.4%,with an increase of 3.1%.关键词
缺陷检测/YOLOv8/FasterNet/注意力机制/特征聚焦扩散金字塔网络Key words
defect detection/YOLOv8/FasterNet/attention mechanism/feature focused diffusion pyramid network分类
矿业与冶金引用本文复制引用
李冠胜,阮景奎,王宸,闫伟伟..基于改进FasterNet-YOLOv8的焊缝表面缺陷检测算法[J].机电工程技术,2026,55(2):78-83,6.基金项目
国家自然科学基金青年项目(51907055) （51907055）
湖北省教育厅项目(B2022275) （B2022275）
中建科技研发课题(CSCEC-2022-Q-52) （CSCEC-2022-Q-52）
湖北省科技厅区域科技创新计划(2023EHA018) （2023EHA018）
