A MIL-based approach for welding defect classification

Welding quality plays a prominent role in many fabrication processes, such as those in the automotive, aerospace, shipbuilding, and electronics industries. Deep learning facilitates the automatic classification of welding quality using non-invasive techniques such as X-rays, thermal imaging, and ultrasonic techniques. In this study, we investigate the ability of deep learning to detect welding quality directly from the welded workpieces. Typically, good and bad weld samples can be seen together along with surrounding material as a background, which makes traditional classification based using computer vision difficult. Therefore, we propose a new framework based on Multiple Instance Learning (MIL) that reveals the distribution of each small piece of the image and serves as an effective guide for welding quality classification. Furthermore, a new collection of welding image datasets is developed to verify the baseline model and our proposed method. The results show that our proposed method is effective in classifying welding quality in welding workpiece images and identifying welding features in each small section of the welding area in workpiece images. This study demonstrates the effectiveness of incorporating MIL in weakly supervised welding quality classification and reveals the potential for performing quality control on welding in industrial pipelines automatically.