Accurate segmentation of small targets for LCD defects using deep convolutional neural networks.

For the purpose of effectively solving the problems of manual inspection of thin‐film transistor–liquid crystal display (TFT‐LCD) with spot Mura high leakage rate and time consuming, this article proposes a visual detection method for LCD defects based on Mask R‐CNN convolutional neural network. In this article, a new feature extraction network is built by fusing ResNet with efficient channel attention (ECA) channel attention mechanism, and a new feature fusion network is constructed by adding ECA channel attention mechanism to Feature Pyramid Network (FPN). In the dataset of self‐made display defect images collected at the production site, the current more advanced segmentation network was compared qualitatively and quantitatively with our method, and the results showed that the proposed method was more accurate in detecting spot Mura, with a detection accuracy of 91%. [ABSTRACT FROM AUTHOR]