基于深度学习的焊缝X射线图像缺陷识别模型

Deep Learning-based Model for Identifying Defects in X-ray Images of Weld Seams

基于Weld-SSD算法模型和Weld-RCNN算法模型,进行焊缝图像缺陷识别算法优化,设计并实现了基于Anchor-based的Weld-RCNN算法,缺陷检测性能召回率达到84.6%,精准率70.2%。建立焊缝X射线图像缺陷识别模型并测试验证该模型的准确性。测试结果表明,算法模型在测试中表现良好,为解决焊缝X射线缺陷检测问题提供新的思路与方法。

Based on Weld-SSD and Weld-RCNN algorithm model,the defect recognition algorithm of welding seam images was optimized,and the Anchor-based Weld-RCNN algorithm was designed and implemented.The recall rate of defect detection performance reached 84.6%,and the accuracy rate was 70.2%.The welding seam X-ray image recognition model was established and tested.The results show that the model performs well and provides new ideas for solving the problem of X-ray defect detection in welding seams.