XDNet: A Few-Shot Meta-Learning Approach for Cross-Domain Visual Inspection.

Automated visual inspection has the potential to improve the efficiency and accuracy of inspection tasks across various industries. Deep learning models have been at the forefront of many automated visual inspection technologies. In this work, we focus on a specific instance of a visual inspection problem: the defect detection and classification problem. Training a deep learning model from scratch to detect defects is challenging due to the scarcity of labeled images with defects. Moreover, it is progressively more challenging to adapt a deep learning model across different domains using limited labeled data. We propose a cross-domain meta-learning framework, XDNet, to solve the defect classification problem using a few labeled samples. XDNet is inspired by recent advancements in pre-trained backbone models as general feature extractors and meta-learning frameworks, which adapt across different domains using non-parametric classifiers under limited computational resources. We demonstrate the efficacy of XDNet using a benchmark anomaly detection dataset which we re-formulate as a defect detection and classification problem. Experimental results suggest that XDNet performs significantly better (≈ 17%) than the existing state-of-the-art and baseline models. Additionally, we perform an ablation study to identify the important components that contribute to the improved performance of the proposed framework. Finally, we conduct a data domain-specific analysis to understand the potential strengths and drawbacks of XDNet on different types of defects.