Vehicular Mini-LED backlight display inspection based on residual global context mechanism  

作　　者：Guobao Zhao Xi Zheng Xiao Huang Yijun Lu Zhong Chen Weijie Guo 

机构地区：[1]Department of Electronic Science,Xiamen University,Xiamen 361000,China

出　　处：《Frontiers of Optoelectronics》2024年第4期81-90,共10页光电子前沿(英文版)

基　　金：National Natural Science Foundation of China(Grant Nos.62275227,62274138,and 11904302);Project of Ministry of Industry and Information Technology of China(Grant No.246);Science and Technology Project of Fujian Province(Grant Nos.2023H4028 and 2023H6038);Key Research and Industrialization Projects of Technological Innovation of Fujian Province(Grant No.2023G043);Shenzhen Science and Technology Program(Grant No.JCYJ20220530143407017).

摘　　要：Mini-LED backlight has emerged as a promising technology for high performance LCDs,yet the massive detection of dead pixels and precise LEDs placement are constrained by the miniature scale of the Mini-LEDs.The high-resolution network(Hrnet)with mixed dilated convolution and dense upsampling convolution(MDC-DUC)module and a residual global context attention(RGCA)module has been proposed to detect the quality of vehicular Mini-LED backlights.The proposed model outperforms the baseline networks of Unet,Pspnet,Deeplabv3+,and Hrnet,with a mean intersection over union(Miou)of 86.91%.Furthermore,compared to the four baseline detection networks,our proposed model has a lower root-mean-square error(RMSE)when analyzing the position and defective count of Mini-LEDs in the prediction map by canny algorithm.This work incorporates deep learning to support production lines improve quality of Mini-LED backlights.

关 键 词：Mini-LED Automated optical inspection Deep learning DISPLAY 

分 类 号：U463.6[机械工程—车辆工程] TN873.93[交通运输工程—载运工具运用工程]