A Few-shot Learning Method for the Defect Inspection of Lithium Battery Sealing Nails

Abstract

Vision-based industrial surface defect detection utilizing computer vision technologies to analyze defects in the appearance of industrial products has become popular in intelligent manufacturing. It makes inspectors move away from inefficient and labor-consuming traditional inspection methods. In this field, sealing nails play a vital role in the power battery of vehicles, and the industrial piece needs strict quality inspection according to its visual appearance before application. However, many difficulties exist, such as the lack of defect samples, low visibility of defects, and irregular shapes in the defect detection of industrial sealing nails. In this paper, we first re-labeled all non-normal areas based on the geometric contour features of the defects and made a practical classification. Second, obtain multi-dimensional image information by the polarization imaging technique; thus, it can effectively cope with low visibility. Third, proposing a new context-based Copy-Paste augmentation approach that can effectively expand the sealing nail dataset and improve the segmentation accuracy. Several experimental results have proven our methods’ accuracy and feasibility in segmentation detection models. For example, the mean pixel accuracy(mPA) criteria enhanced by about 14.9% compared with traditional methods.