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.