Citation: Xie, W.; Chen, H.; Wang, Z.;
Liu, B.; Shuai, L. Display Line Defect
Detection Method Based on Color
Feature Fusion. Machines 2022, 10,
723. https://doi.org/10.3390/
machines10090723
Academic Editors: Yuansong Qiao
and Seamus Gordon
Received: 16 July 2022
Accepted: 22 August 2022
Published: 24 August 2022
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Article
Display Line Defect Detection Method Based on Color
Feature Fusion
Wenqiang Xie
1
, Huaixin Chen
1,
*, Zhixi Wang
1,2
, Biyuan Liu
1
and Lingyu Shuai
1
1
Department of Resources and Environment, University of Electronic Science and Technology of China,
Chengdu 611731, China
2
Novel Product R & D Department, Truly Opto-Electronics Co., Ltd., Shanwei 516600, China
* Correspondence: huaixinchen@uestc.edu.cn
Abstract:
Display color line defect detection is an important step in the production quality inspection
process. In order to improve the detection accuracy of low contrast line defects, we propose a display
line defect detection method based on color feature fusion. The color saliency channels in the RG|GR
and BY|YB channels were obtained using the relative entropy maximum criterion. Then, RG|GR
were combined with the a channel and BY|YB with the b channel to calculate the red-green and the
blue-yellow color fusion maps. The fusion color saliency map of the red-green and the blue-yellow
color fusion maps was obtained by color feature fusion. Finally, the segmentation threshold was
calculated according to the mean and standard deviation of the fusion color saliency map. The fused
color saliency map was binarized and segmented to obtain a binary map of color line defects. The
experimental results show that for the detection of multi-background offline defects, the detection
accuracy of the algorithm in this paper is better than 90%, while other mainstreams fail to detect.
Compared with state-of-the-art saliency detection algorithms, our method is capable of real-time
low-contrast line defect detection.
Keywords: liquid crystal display; defect detection; line defect; salient color feature; feature fusion
1. Introduction
The quality inspection of display products plays an important role in the production
process. Existing display quality inspection uses Automated Optical Inspection (AOI)
equipment for manual feeding to automatically detect defects in the display. The detection
accuracy of AOI equipment depends on the corresponding algorithm, which limits the
application of AOI technology [
1
]. Industrial production still relies on manual detection,
which has low efficiency and unstable accuracy due to the influence of subjective feelings
of testers [
2
]. In particular, color low-contrast defects are difficult to detect by the human.
Therefore, using machine vision and digital image processing technology to achieve display
defect detection has become an urgent problem to be solved.
Display defect detection technology has made great progress. Existing display defect
detection methods are mainly divided into three types: methods based on image registra-
tion [
3
,
4
], background reconstruction [
5
–
10
] and deep learning [
11
–
17
]. Zhang et al. [
4
]
utilized PatMax and image correction technology based on affine transformation to solve
the problem of incompletely aligned pixel edges when the template image is registered
with the test image. The method improves the accuracy of defect detection but cannot
extract the features from the uniform background for the registration process. Ma et al. [
7
]
proposed using the Gabor filter to remove noise and background from display images.
Aiming at the problem of low defect contrast, the method utilizes the optical properties
in printing to obtain images with the smallest contrast by blurring to achieve background
reconstruction. This method can detect low contrast defects, but requires strict display
background conditions and cannot be used in multi-display backgrounds. Lin et al. [
11
]
Machines 2022, 10, 723. https://doi.org/10.3390/machines10090723 https://www.mdpi.com/journal/machines
