摘要
为了克服可见光视觉检测在焊接接头表面缺陷识别中易受光照变化、背景单一以及缺陷尺寸小、特征不明显等因素影响,而导致检测精度不足的问题,提出了一种基于红外成像与深度学习的焊缝表面缺陷检测方法.采用红外热像仪采集铝合金搅拌摩擦焊接头焊后余温阶段的温度场信息,构建红外图像缺陷数据集,并基于YOLO模型实现焊缝表面缺陷的分类与定位识别.针对红外图像中小尺度缺陷检测困难的问题,对模型结构进行优化,引入注意力机制以抑制温度场中与缺陷无关的全局热扩散信息,突出与缺陷相关的局部特征.结果表明,该方法在检测精度与稳定性方面具有良好性能,能够满足焊缝表面缺陷快速检测的应用需求.
To address the problem of insufficient detection accuracy of visual detection by visible light in surface defect identification of welded joints,which is mainly susceptible to factors,such as illumination variations,simple backgrounds,small defect sizes,and inconspicuous features,a weld surface defect detection method based on infrared imaging and deep learning was proposed.An infrared thermal imager was used to acquire temperature field information of joints of aluminum alloy by friction stir welding during the post-weld residual heat stage,and an infrared image defect dataset was established.Based on the YOLO model,classification and localization identification of weld surface defects were realized.To address the problem of difficulty in smallscale defect detection of infrared images,the model’s structure was optimized by introducing an attention mechanism,which suppressed global thermal diffusion information irrelevant to defects in the temperature field and enhanced defect-related local features.The results indicate that the proposed method achieves good performance in detection accuracy and stability,demonstrating its applicability for rapid weld surface defect detection.
作者
任大鑫
傅胜楷
李子充
宋刚
张兆栋
刘黎明
REN Daxin;FU Shengkai;LI Zichong;SONG Gang;ZHANG Zhaodong;LIU Liming(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China;Liaoning Provincial Key Laboratory of Advanced Welding and Joining Technology,Dalian 116024,China)
出处
《焊接学报》
北大核心
2026年第2期20-28,54,共10页
Transactions of The China Welding Institution
基金
国家重点研发计划(2022YFB4600900)
国家自然科学基金资助项目(52275313)。
关键词
深度学习
红外成像
搅拌摩擦焊
缺陷检测
deep learning
infrared imaging
friction stir welding
defect detection
