Automated weld defect detection using gated attention and squeeze and excitation fusion U-Net

Welding is a fundamental process in manufacturing and construction, playing a pivotal role in joining materials by melting and fusing them. This versatile technique finds widespread application across diverse industries, creating robust and structurally sound components. Regular inspection and continual oversight of the welding process are imperative. Manual inspection methods, however, prove inadequate in addressing the ever-growing demand for safety and precision. In recent years, integrating deep learning and computer vision technologies has revolutionized the welding field by enhancing and automating inspection and safety protocols. This paper proposes DeepFuse WeldNet, an automated Weld detection Framework that employs a novel Gated Attention Squeeze and Excitation Fusion U-net (GASEUNet) for weld defect segmentation. Our research focuses on enhancing the accuracy of the model for more precise identification and measurement of weld porosity within images. The model provides a significant boost in segmentation accuracy that is better than existing methods. Our experimental results demonstrate the effectiveness of GASEUNet, with an impressive Jaccard Coefficient (JC) of 98.12. The proposed DeepFuse WeldNet also consists of a fully automated Weld Inspection Framework that classifies the weld defects as safe or unsafe by comparing the measurements to the standards provided by the American Welding Society (AWS)