Environmentally-Robust Defect Classification With Domain Augmentation Framework

Visual defect classification is a critical process in manufacturing systems, aiming to achieve high-quality production and reduce costs. Although deep learning-based defect classification models have achieved significant success, their performance can be significantly diminished due to 'environment shifts' - variations in manufacturing environments across multiple production lines. To address this challenge, we propose a domain augmentation framework to construct an environmentally-robust defect classification model, delivering high performance across various manufacturing environments using a training dataset from only a single production line. In our framework, each environment is treated as a separate domain, and multiple augmented domains are created using image transformation functions. Subsequently, a defect classification model is trained using a multi-source domain generalization (DG) method with these augmented domains. This approach mitigates the single-source DG problem to a multi-source DG problem, enabling the adoption of multi-source DG methods, which leads to performance improvements. The effectiveness of the proposed framework is demonstrated through experiments on a dataset provided by a Korean manufacturing company.