Deep Learning-Based Multi-Species Appearance Defect Detection Model for MLCC

The appearance of defects in a multilayer ceramic capacitor (MLCC) adversely affects its performance and reliability. Thus, detecting these defects during MLCC production is imperative. However, this task faces numerous challenges, such as significant variations in the shape and size of defects, indistinct defect boundaries, and the inefficiency of manual detection methods. To address these issues, this article proposes the RSE-YOLO model for identifying, localizing, and classifying defects in MLCC images. We design a novel backbone structure, namely the residual coordinate weighted convolutional network (RCWCNet), which possesses enhanced feature information extraction capabilities for accurately locating defect areas. Additionally, we introduce the space attention pyramid pooling module (SAPPM) to achieve a weighted fusion of local and global feature information. Furthermore, the ECA-PAN is employed as the model’s neck structure to facilitate the fusion of feature information at different scales, improving the model’s generalization ability in multiscale defect detection. Experimental results demonstrate that the RSE-YOLO model exhibits excellent performance on the MLCC dataset, achieving an mAP50 of 93.9%, mAP50-95 of 63.2%, F1 of 90.9%, and a frame rate of 57 FPS, meeting the requirements for the task of MLCC appearance defect detection.