Research on Weld Defect Detection and Evaluation Technology based on Deep Learning

Abstract

Aiming at the problems of low efficiency and strong subjectivity in the current detection of weld defects by radiographic imaging technology, an object detection method of weld defects based on multi-channel fusion convolutional neural network is proposed. In this method, the images of weld defects are encoded and input into multiple feature extraction channels formed by parallel fusion of CNN. After that, the extracted features are fused with full connection layer and the feature vectors are output. Finally, the final output is obtained by Softmax for classification. The proposed method is verified by weld defect images in actual production. The experimental results indicate that the mAP of the multi-channel fusion convolutional neural network reaches 76.37%, and the detection accuracy of weld defects is higher than that of other network such as ResNet-50 and VGG-16. The proposed method can be applied to X-ray intelligent detection of weld defects and other scenarios.