摘要
为提高γ射线焊缝底片缺陷检测的可靠性,提出一种基于改进YOLOv5s的焊缝缺陷检测模型,实现了在复杂环境下对焊缝底片缺陷的高效准确检测.针对原YOLOv5s模型中卷积网络存在的大量通道和冗余信息问题,首先,在主干网络的C3模块中融入了SCCONV网络模块,减少了冗余信息并提高模型检测性能;其次,考虑到焊缝缺陷的形态多样、大小不一以及对比度低等特点,引入了卷积注意力模块(CBAM和SE),以增强模型对感兴趣区域的关注度;最后,在边界框回归检测中,采用EIoU损失函数替代传统YOLOv5s中的CIoU损失函数,显著提升了模型的检测精度和鲁棒性.结果表明,改进后的模型在精确度、召回率等指标上均较传统YOLOv5s算法有显著提升,具体表现为准确率提高4.2%、召回率提高3.2%、平均精度均值提高3.4%,进而验证了该方法在γ射线焊缝缺陷检测中的有效性.
To enhance the reliability ofγ-ray radiographic film defect detection of welds,a defect detection model based on improved YOLOv5s was proposed,achieving efficient and accurate detection of radiographic film defects of welds in complex environments.In response to excessive channels and redundant information in the convolutional network of the original YOLOv5s model,firstly,the SCCONV network module was integrated into the C3 module of the backbone network,reducing redundant information and boosting the detection performance of the model.Secondly,in light of the characteristics of weld defects,such as morphological diversity,scale variation,and low contrast,convolutional attention modules(CBAM and SE)were introduced to enhance the attention of the model to the region of interest.Finally,the EIoU loss function was employed to replace the CIoU loss function in the traditional YOLOv5s in the bounding box regression detection,significantly improving detection precision and robustness of the model.Experimental results demonstrate that the improved model has significant improvements in the indices such as precision and recall rate,compared with the traditional YOLOv5s algorithm.They can be manifested in the fact that the accuracy is increased by 4.2%;the recall rate is increased by 3.2%;the mean average precision is increased by 3.4%.The effectiveness of the method in theγ-ray defect detection of welds was verified.
作者
王树强
芦伟
张宽
李君伟
WANG Shuqiang;LU Wei;ZHANG Kuan;LI Junwei(School of Mechanical and Power Engineering,Shenyang University of Chemical Technology,Shenyang 110142,China)
出处
《焊接学报》
北大核心
2026年第2期116-125,共10页
Transactions of The China Welding Institution
基金
辽宁省教育厅高校基本科研面上项目(LJKMZ20220774)
沈阳化工大学2022年研究生精品示范课程项目
教育教学改革研究项目。
