An antagonistic training algorithm for TFT-LCD module mura defect detection

Abstract

Although the production process of liquid crystal display model has been automated, the quality detection still depends on manual work. Mura defect is one of the common defects appearing in TFT-LCD modules. Since mura defect is not significantly different from the common background, it is difficult to detect. This paper presents a deep channel attention-based classification network (DCANet), which acts as a powerful feature extractor for object detectors, and proposes an antagonistic training algorithm based on convolution neural network. By the proposed training approach, the deep learning-based object detectors can achieve high accuracy even with a small number of training samples of mura defect. The experimental results show that compared to vanilla training method, the deep learning-based detectors trained by our proposed method could significantly improve their performance on mura defect detection with a few training samples. Even trained on only 600 samples, the mistake rate and miss rate are only 8.08% and 0.267% respectively, which can completely fulfill the enterprise’s requirements of 10% and 0.3%.