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

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 Cascade-RCNN network model. The CCBFE-RCNN model was tested using an industrial defect detection dataset to verify its generalization ability, which can provide algorithm support for intelligent recognition of weld defects.