Detection and segmentation framework for defect detection on multi‐layer ceramic capacitors

Abstract Detecting defective multi‐layer ceramic capacitors (MLCCs) during the inspection stage is a crucial production task to effectively manage production yield and maintain quality. However, this task presents two challenges: the necessity of pixel‐level segmentation in high‐resolution images and unexplored defect patterns. To address these challenges, this paper introduces an MLCC defect‐detection framework based on deep learning with an MLCC dataset we constructed and a comprehensive analysis of MLCC images. Our framework employs an object‐detection model to identify dielectric regions in input MLCC images, followed by a semantic segmentation model to create dielectric masks for calculating the margin ratio. This approach follows the traditional inspection process but can be performed without specialized personnel. Furthermore, we generated pseudo‐defect images using generative adversarial networks to obtain sufficient training data. Experiments demonstrate the effectiveness of our framework, which achieved a defect‐detection accuracy of 93.1%, as revealed by an in‐depth error analysis.