Defect detection in welding radiographic images based on semantic segmentation methods

In order to remove the limitations of human interpretation, many computer-aided algorithms have been developed to automatically detect defects in radiographic images. Compared with traditional detection algorithms, deep learning algorithms have the advantages of strong generalization ability and automatic feature extraction, and have been applied in welding defect detection. However, these algorithms still need further research in the acquisition and cleaning of welding radiographic image data, the selection and optimization of deep neural networks, and the generalization and interpretation of network models. Therefore, this paper proposes an automatic welding defect detection system based on semantic segmentation method. Firstly, a dataset of radiographic images of welding defects, called RIWD, is set up, and the corresponding data preprocessing and annotation methods are designed for the training and evaluation of the algorithm. Secondly, an end-to-end FPN-ResNet-34 semantic segmentation network-based defect detection algorithm is implemented, and the network architecture is experimentally demonstrated to be suitable for defect features extraction and fusion. Thirdly, to improve the detection performance of the algorithm, an optimization strategy for the network is designed according to the data characteristics of defects, which includes data augmentation based on combined image transformations and class balancing using a hybrid loss function with dice loss and focal loss. Finally, to ensure the reliability of the algorithm, the generalization ability of the algorithm is tested using external validation, and the defect features learned by the network are visualized by post-interpretation technique. The experimental results show that our method can correctly discriminate defect types and accurately describe defect boundaries, achieving 0.90 mPA, 0.86 mR, 0.77 mF1 and 0.73 mIoU, which can be applied to automatically interpret radiographic images.