基于CCBFE-RCNN模型的焊缝X射线图像缺陷识别算法  

Defect identification algorithm for weld X-ray images based on the CCBFE-RCNN model

作　　者：戴铮 刘骁佳 潘泉[1] DAI Zheng;LIU Xiaojia;PAN Quan(School of Automation,Northwestern Polytechnical University,Xi'an,710072;Shanghai Spaceflight Precision Machinery Institute,Shanghai,201600)

机构地区：[1]西北工业大学,自动化学院,西安710072 [2]上海航天精密机械研究所,上海201600

出　　处：《焊接学报》2025年第1期24-33,共10页Transactions of The China Welding Institution

摘　　要：针对X射线图像人工评定过程中存在劳动强度大、检测效率低等问题,提出一种基于Cascade-RCNN网络改进的多尺度目标检测网络CCBFE-RCNN模型,采用合并卷积层结构和FPN特征金字塔网络提升模型特征提取的尺度范围,增强了模型对于多尺度特征的提取能力;使用BFE特征批量消除网络,随机消除特征图区域,避免多次训练过程中的过拟合问题并强化了特征区域表达,同时对损失函数进行改进,对模型没有准确识别出含缺陷图像加大惩罚.通过构建并扩充熔焊焊缝X射线图像数据集,对模型进行测试.结果表明,CCBFE-RCNN缺陷检测模型全类别召回率均值、全类别精确率均值为93.09%和91.92%,与Cascade-RCNN网络模型相比平均召回率提升5.16%,平均精确率提升5.27%.并使用工业缺陷检测数据集对CCBFE-RCNN模型进行测试,验证了模型的泛化能力,可为焊缝缺陷智能化识别提供算法支撑.In view of the problems of high labor intensity and low detection efficiency in the manual evaluation process of X-ray images,a multi-scale object detection network CCBFE-RCNN model based on the improved Cascade-RCNN network is proposed.The combined convolutional layer structure and FPN feature pyramid network are used to enhance the scale range of model feature extraction,and the model's ability to extract multi-scale features is enhanced;Use BFE features to batch eliminate networks,randomly eliminate feature map regions,avoid overfitting problems during multiple training processes,and enhance feature region expression.At the same time,improve the loss function to increase penalties for models that do not accurately identify images containing defects.The model was tested by constructing and expanding a dataset of X-ray images of fusion welds The results show that the CCBFE-RCNN defect detection model has an average recall rate of 93.09%and an average precision rate of 91.92%across all categories,which is 5.16%higher than the average recall rate and 5.27%higher than the average precision rate of the CascadeRCNN network model.The CCBFE-RCNN model is tested using an industrial defect detection dataset to verify its generalization ability,which can provide algorithm support for intelligent recognition of weld defects.

关 键 词：缺陷识别 合并卷积 损失函数 召回率 精确率 

分 类 号：TG409[金属学及工艺—焊接]