Machine Vision-Based Lightweight Multiscale Automatic Defect Segmentation Network for MLCC

Defect detection in multilayer ceramic capacitors (MLCCs) is critical for ensuring the production quality. However, surface defect detection in MLCC faces challenges, such as multiscale defects, indistinct boundaries, and limited availability of defect samples. To address these issues, this article proposes an automated defect detection system based on machine vision. The system includes a microscopic imaging unit (MIU) for defect image capture, a motion control unit (MCU) for MLCC movement control, and an image processing unit (IPU) for efficient MLCC surface defect segmentation. In addition, a lightweight multiscale network (LMS-net) is introduced for defect segmentation. LMS-net aims to effectively capture image context information, extract richer semantic details, and handle class imbalance. It incorporates a deep feature extraction module (DFEM) to improve the feature extraction capabilities and reduce computational burden, a dual attention (DA) to enhance attention to smaller defect areas, and a cascaded pyramid feature fusion module (CPFFM) to incorporate context information at different scales. In addition, a combined loss function is used to handle the class imbalance during the training process. Experimental results demonstrate the outstanding performance of the proposed network on the MLCC surface defect dataset, achieving a 94.14% dice similarity coefficient (DSC), a 67.01% mean intersection over union (MIoU), and 86.41% mean pixel accuracy (MPA), all with a smaller parameter.