A new method to reduce the false detection rate of ceramic tile surface defects online inspection system

Abstract

The application of intelligent inspection to the online inspection of ceramic tile surface defects not only ensures the stability of product quality but also reduces the cost of labor for enterprises. Existing detection methods often achieve good results for the detection of defects in known background textures, however, its false detection rate increases dramatically, and its practicality is greatly reduced when there are unknown background textures or defects with similar characteristics to known background textures. This paper proposes a non-defective and defective samples synchronous comparison detection(N-DSCD) algorithm which combines traditional detection with deep convolutional neural network(DCNN). Firstly, a reference image library of non-defective ceramic tiles which contains all random backgrounds of the same batch of tiles is constructed based on DCNN extracting image features in real-time. After that, a reference image with the most similar features to the image to be detected is searched out from the image library relying on the feature comparison method. Next, we feed the image to be detected and the reference image to the defect detector at the same time, the difference in results is used as the basis of judging whether the ceramic tile has defects or not. Tested on a total of 12,000 images of ceramic tiles with 20 different textures found that: the computational time of a single ceramic tile increased by only 120 ms, and the average false detection rate of 20 unknown background texture tiles was reduced from 23.1% to 5.8%. It greatly improves the practicality of the ceramic tile surface defects online inspection system and has wider economic benefits.