Defect detection of low-resolution ceramic substrate image based on knowledge distillation

Ceramic substrate is a vital foundational material of electronic devices， and implementing defect detection for ceramic substrates using machine vision technology combined with deep learning strategies holds significant importance in ensuring product quality. Increasing the field of view of the imaging equipment to make simultaneous imaging of multiple ceramic substrates possible can significantly improve the detection speed of a ceramic substrate. However， it also results in decreased image resolution and subsequently reduces the accuracy of defect detection. To solve these problems， a low-resolution ceramic substrate defect automatic detection method based on knowledge distillation is proposed. The method utilizes the YOLOv5 framework to construct a teacher network and a student network. Based on the idea of knowledge distillation， high-resolution image feature information obtained by the teacher network is used to guide the training of the student network to improve the defect detection ability of the student network for low-resolution ceramic substrate images. Moreover， a feature fusion module based on the coordinate attention （CA） idea is introduced into the teacher network， enabling it to learn features that adapt to both high-resolution and low-resolution image information， thus better guiding the training of the student network. Finally， a confidence loss function based on the gradient harmonizing mechanism （GHM） is introduced to enhance the defect detection rate. Experimental results demonstrate that the proposed ceramic substrate defect detection method based on knowledge distillation achieves an average accuracy and average recall of 96.80% and 90.01%， respectively， for the detection of five types of defect-stain， foreign matter， gold edge bulge， ceramic gap， and damage-in low-resolution （224×224） input images. Compared with current mainstream object detection algorithms， the proposed algorithm achieves better detection results. © 2023 Chinese Academy of Sciences. All rights reserved.