机电工程技术2026，Vol.55Issue(11)：52-58,81,8.DOI:10.3969/j.issn.1009-9492.2026.11.009
基于DEEW-YOLO的焊缝X射线图像缺陷检测方法
Weld X-ray Image Defect Detection Algorithm Based on DEEW-YOLO
李聚龙 1陈科鹏 1李大红 2晏涛1
作者信息
- 1. 湖北文理学院 机械工程学院,湖北 襄阳 441053||智能制造与机器视觉襄阳市重点实验室,湖北 襄阳 441053
- 2. 中国化学工程第六建设有限公司,湖北 襄阳 441053
- 折叠
摘要
Abstract
During welding processes,various defects may occur in pressure vessels due to production techniques and environmental factors,which can compromise the mechanical properties and service life of welded structures.By employing deep learning methods for automated X-ray defect detection,convolutional neural networks(CNNs)can adaptively extract defect information,enabling efficient identification of different weld defect types while reducing human-induced subjective biases.However,most current CNN models still face challenges such as false positives,missed detections,and low recognition rates for minor defects.To address these issues,the DEEW-YOLO algorithm—an enhanced version of YOLOv8n for weld defect detection is proposed.The DEConv_C2f module integrates Detail-Enhanced Convolution(DEConv)with a two-convolution faster implementation of CSP bottleneck(C2f)to improve local detail capture and feature representation.Additionally,the ECA attention mechanism is incorporated into critical network layers to enhance cross-channel information capture.The WIoU loss function ensures balanced contributions between high-quality and low-quality anchor boxes while improving boundary prediction accuracy.The DEEW-YOLO model achieved mAP@0.5,Precision,Recall and FPS parameters of 59.7%,69.0%,58.9%and 144.9 f/s respectively,significantly outperforming the original YOLOv8n model.关键词
X射线图像/焊缝缺陷/YOLOv8n/深度学习Key words
X-ray image/weld defects/YOLOv8n/deep learning分类
信息技术与安全科学引用本文复制引用
李聚龙,陈科鹏,李大红,晏涛..基于DEEW-YOLO的焊缝X射线图像缺陷检测方法[J].机电工程技术,2026,55(11):52-58,81,8.
