Swish-ResNet Method for Faulty Weld Detection

Welding is a fundamental process commonly used in construction and manufacturing industries to effectively join different objects or materials together. There is a growing demand for accurate and dependable weld classification methods due to the increasing complexity and variety of welding applications. Various computer vision and machine learning (ML) based methods have been applied to identify issues with faulty welds. Deep learning (DL) methods typically outperform conventional ML approaches but require more data to train effectively and can overfit when data is limited. In this work, we propose an end-to-end DL method called “swish-ResNet” for reliable classification of good and faulty welds using limited data. We used data augmentation techniques to increase sample diversity and quantity. Furthermore, we employed swish activation in the ResNet-50 model to address issues related to saturation and overfitting. Specifically, we enhanced the ResNet-50 model by incorporating swish activation to improve its ability to detect complex patterns. Additionally, we added four additional dense layers at the end to refine keypoint selection in our method. Experiments conducted on two diverse datasets demonstrate the effectiveness of our swish-ResNet model for the reliable detection of faulty welds.