Concatenated Network Fusion Algorithm (CNFA) Based on Deep Learning: Improving the Detection Accuracy of Surface Defects for Ceramic Tile

Abstract

The low accuracy of detection algorithms is one impediment in detecting ceramic tile’s surface defects online utilizing intelligent detection instead of human inspection. The purpose of this paper is to present a CNFA for resolving the obstacle. Firstly, a negative sample set is generated online by non-defective images of ceramic tiles, and a comparator based on a modified VGG16 extracts a reference image from it. Disguised rectangle boxes, including defective and non-defective, are acquired from the image to be inspected by a detector. A reference rectangle box most similar to the disguised rectangle box is extracted from the reference image. A discriminator is constituted with a modified MobileNetV3 network serving as the backbone and a metric learning loss function strengthening feature recognition, distinguishing the true and false of disguised and reference rectangle boxes. Results exhibit that the discriminator appears to have an accuracy of 98.02%, 13% more than other algorithms. Furthermore, the CNFA performs an average accuracy of 98.19%, and the consumption time of a single image extends by only 64.35 ms, which has little influence on production efficiency. It provides a theoretical and practical reference for surface defect detection of products with complex and changeable textures in industrial environments.