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

doi:10.1007/s12200-024-00140-4

Front. Optoelectron.

2024, Vol. 17

Issue (4) : 35 https://doi.org/10.1007/s12200-024-00140-4

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

Guobao Zhao, Xi Zheng, Xiao Huang, Yijun Lu, Zhong Chen, Weijie Guo(

)

Department of Electronic Science, Xiamen University, Xiamen 361000, China

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Abstract

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.

Keywords Mini-LED Automated optical inspection Deep learning Display

Corresponding Author(s): Weijie Guo

Issue Date: 19 November 2024

Cite this article:

Guobao Zhao,Xi Zheng,Xiao Huang, et al. Vehicular Mini-LED backlight display inspection based on residual global context mechanism[J]. Front. Optoelectron., 2024, 17(4): 35.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-024-00140-4
https://academic.hep.com.cn/foe/EN/Y2024/V17/I4/35

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