Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization
Xuhesheng Chen, Mingyue Liu, Yongjie Niu, Xukang Wang, Ying Cheng Wu
Abstract
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task Learning, and Iteration Learning. Leveraging a steel surface defect dataset as foundational knowledge, our approach compensates for the limited lithium-specific data and enhances model generalization. We also introduce the Lithium Electronic Surface Defect Classification (IESDC) dataset, demonstrating significant accuracy improvements over baseline methods. Our comprehensive evaluation covers model interpretability, robustness, and adaptability. Beyond battery technology, this methodology offers a framework for data scarcity challenges in various industries, emphasizing the importance of adaptable learning methods.