An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection

Abstract

Accurate welding defect location is of great significance to modern manufacturing, which could be used for accurate quality evaluation and precise repairing decision-making basis of different products. Nevertheless, accurate welding defect location is still a challenging task due to some complex factors, such as complex backgrounds, low contrast, weak texture, and class imbalance issue. Recently, deep learning has got great development due to its strong feature expression ability, which has been widely applied into defect detection, but it still exists certain shortcomings on segmentation tasks with the class unbalanced issue or microdefects. To address these issues, with the encoder&#x2013;decoder network architecture, a novel welding defect location method is proposed with an attention-guided segmentation network. To reduce the contextual information loss of the deep encoder module after multiple convolution and pooling operations, a multiscale feature fusion block is proposed to embed into a U-shaped network (U-Net) to acquire more information. On the basis, combined with a bidirectional convolutional long short-term memory (BiConvLSTM) block, an improved attention block is integrated into the skip connections between the encoder path and the decoder path to capture the global, long-range contexts and emphasize target regions, contributing to locate welding defect areas and enhance the segmentation ability on microdefects. Meanwhile, to address the foreground&#x2013;background class imbalance issue, a hybrid loss function combined with binary cross-entropy (BCE) and <inline-formula> <tex-math notation="LaTeX">$D_{\text {ice}}$ </tex-math></inline-formula> loss functions is proposed to effectively utilize their unique excellent characteristics for accurate defect segmentation. Experiment results on the public <i>GDXray</i> dataset show that the proposed segmentation method could obtain a competitive segmentation performance compared with other advanced segmentation models.