Visual Inspection of Welding Zone by Boundary-Aware Semantic Segmentation Algorithm

The segmentation effect of welding zone is vulnerable to image quality and target shape, which brings difficulty for the radiographic inspection of water-cooled pipe. A boundary-aware semantic segmentation algorithm is proposed to improve the precision and robustness of the welding zone inspection system. First, a convolutional neural network and atrous spatial pyramid pooling are employed to generate the feature map of the input image. Second, a boundary distance field (BDF) regression module is designed to predict the distance fields of the inner and outer boundaries, which can reflect the continuous boundary information of welding zone. Then, spatial attention weight is calculated according to the regressed boundary distance fields, and the calculated weight is fused into the feature map to enhance the significance of welding zone. Finally, the segmentation mask is predicted based on the fused feature map. Experiments indicate that the guidance of boundary information in the semantic segmentation algorithm can effectively reduce the interference of nonweld zones. The segmentation accuracy of the welding zone is 93.70%, and average detection time of a single image is less than 11 ms.