A Transfer Learning approach for Automatic Welding Defect Detection

This work presents a smart monitoring system capable of performing accurate quality checks to detect welding defects in fuel injectors. It employs classical computer vision for the geometrical analysis and Deep Neural Network for the surface quality analysis. Despite the few training samples, the network has been trained successfully leveraging on the transfer learning and data augmentation techniques obtaining an accuracy of 97,22%.