Improving Robustness of Defect Detection models using Adversarial-based Data Augmentation

We propose an adversarial-based data augmentation method to improve the robustness of object detection models, specifically for industrial defect detection.Unlike prior approaches focused on classification or synthetic datasets, our method generates adversarial examples that target both classification and localization outputs.We further introduce controlled white noise to these examples, enhancing robustness against environmental variations.Empirical evaluation on a real-world dataset of defective laser welding images shows that our approach outperforms standard data augmentation and existing adversarial training methods, improving both model accuracy and resilience to diverse perturbations encountered in real-world settings.