华侨大学学报（自然科学版）2025，Vol.46Issue(5)：505-512,8.DOI:10.11830/ISSN.1000-5013.202508018
采用改进YOLOv9的X射线图像焊缝缺陷检测算法
X-Ray Image Weld Defect Detection Algorithm Using Improved YOLOv9
摘要
Abstract
To address the issues of low efficiency and strong subjectivity in traditional methods for weld defect detection,as well as the shortcomings of existing deep learning models in recognizing tiny defects,resisting complex background interference,and adapting to multiple scales,this paper proposes an improved YOLOv9-based X-ray weld defect detection algorithm,named YOLOv9s-GMS.The algorithm introduces the GhostConv module to enhance the ability of extracting tiny features,the added single-head self-attention(SHSA)module is to focus on defect regions while suppressing background interference.The MSDCA module is to strengthen multi-scale weld feature representation.In addetion,a Shape-IoU loss function is employed to improve the localization accuracy of irregular defects.Experiments results on the HWDXray dataset show that the YOLOv9s-GMS achieves an mAP@0.5 of 0.954,an F1-score of 0.933,and a recall of 0.903,significantly outperforming algorithms such as YOLOv5,YOLOv9s and Faster R-CNN.These results demonstrate that the proposed method effectively improves accuracy,robustness,and multi-scale adaptability in weld defect detec-tion.关键词
焊缝缺陷检测/微小缺陷/YOLOv9算法/深度学习Key words
weld defect detection/tiny defect/YOLOv9 algorithm/deep learning分类
计算机与自动化引用本文复制引用
唐凯,林宁,林振超,黄凯,郑力新..采用改进YOLOv9的X射线图像焊缝缺陷检测算法[J].华侨大学学报（自然科学版）,2025,46(5):505-512,8.基金项目
福建省科技计划项目(2020Y0039) （2020Y0039）
福建省泉州市科技计划项目(2020C0042R) （2020C0042R）
