Citation: Wang, Z.; Xie, W.; Chen, H.;
Liu, B.; Shuai, L. Color Point Defect
Detection Method Based on Color
Salient Features. Electronics 2022, 11,
2665. https://doi.org/10.3390/
electronics11172665
Academic Editor: Silvia Liberata Ullo
Received: 20 July 2022
Accepted: 23 August 2022
Published: 25 August 2022
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Article
Color Point Defect Detection Method Based on Color
Salient Features
Zhixi Wang
1,2
, Wenqiang Xie
1
, Huaixin Chen
1,
*, 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 point defect detection is an important link in the display quality inspection
process. To improve the detection accuracy of color point defects, a color point defect detection
method based on color salient features is proposed. Color point defects that conform to the perception
of the human vision are used as the key point for detection. First, the human visual perception
constraint coefficient is used to correct the RGB three-channel image to obtain the color-channel-
transformed image. Then, the local contrast method is used to extract the point features of the color
channel, which achieves point defect enhancement, noise and background suppression. Finally,
the mean and standard deviation of the defect feature maps of R, G, and B channels are calculated.
The maximum mean and standard deviation are selected as thresholds using the maximum fusion
criterion to perform binarization segmentation of the defect feature maps of R, G, and B channels. An
OR operation was performed on the segmented images and the point defect segmentation results
were combined. The experimental results show that the average detection accuracy and recall of
the algorithm is higher than 94%, which is a significant improvement compared with mainstream
detection methods and meets the needs of industrial production.
Keywords: defect detection; liquid crystal display; color feature; local contrast; fusion
1. Introduction
The development of information technology has increased the demand for displays.
Especially with the advent of the 5G information era, smartphones, as terminal products,
can no longer fully meet the needs of people’s production and life. The new generation of
flexible displays and smart wearable devices bring new challenges and requirements to the
development of display technology. This has led to an upgrade in the production of existing
displays, in which machine vision technology is used to improve the productivity and
reduce the production costs of factories [
1
]. The factory quality inspection of the displays
is partially automated by automated optical inspection (AOI) equipment. However, the
defect detection algorithm of AOI equipment limits its application. The detection of display
defects in industrial production still relies on human eyes for detection, which hinders the
production efficiency [
2
]. Therefore, developing a more effective defect detection algorithm
to achieve machine vision instead of manual inspection is a pressing issue.
Display defect detection techniques have made great progress. The existing display
defect detection methods are divided into three main categories: image registration-based
methods [
3
–
5
], background reconstruction [
6
–
12
] and deep learning [
13
–
19
]. Zhu et al. [
3
]
proposed the use of Fourier-Mellin Transform for the coarse registration of images, the
use of accelerated robust features for precise registration of images, which improved the
accuracy of the registration method. However, the image registration-based method can-
not align images with simple backgrounds. Sun et al. [
10
] proposed a defect detection
method using cascade mean shift and level set algorithm, where mean shift was used
to detect defect candidate regions and solve the problem of the level set method being
Electronics 2022, 11, 2665. https://doi.org/10.3390/electronics11172665 https://www.mdpi.com/journal/electronics
