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摘要
各类显示屏中Mura缺陷的自动识别和定位对提高显示屏幕的产品品质具有重要作用，是当前迫切需要发展的重要技术之一。针对当前手机屏幕Mura缺陷对比度低、缺乏明显边缘等特点，文中提出一种基于图像灰度曲线的Mura缺陷检测方法及其改进方法。改进方法基于均值滤波平滑和降采样原理，通过研究采样线上灰度曲线的波峰与波谷信息，利用BP神经网络构建线Mura缺陷的自动检测和定位算法。结合真实手机屏幕图像验证结果表明，与现有多种Mura缺陷检测方法相比，本文的改进方法能更准确快速地识别手机屏幕中的线Mura缺陷，识别准确率达到98.33%，检测过程无需调节参数，实现了线Mura缺陷的自动检测和定位。
Abstract
Automatic identification and location of Mura defect in various screens plays an important role in improving the quality of screens. It is one of the most important technologies that need to be developed urgently. Aiming at the features of low contrast and lack of obvious edge of Mura defect, this paper proposes a method of Mura detection based on image gray curve and its improved method. This improved method is based on the principle of mean filter to smooth the picture and down-sampling. By studying the information about peak and trough of the gray curve on sampling lines, the BP neural network is used to construct an automatic detection and location algorithm for line Mura. The experimental results show that, compared with the existing Mura detection methods, the improved method in this paper can distinguish line Mura defect on the mobile phone screen more accurately and quickly. The accuracy rate is 98.33%, and no parameter needs to be adjusted during the detection process, realizing automatic detection, and positioning of line Mura.
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Key words:
- BP neural network /
- gray scale curve /
- Mura /
- defect detection /
- image processing
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Overview
Overview: Since the screen of the mobile phone has complex structures, and the manufacturing process is complicated, point Mura, and line Mura tend to arise when a screen is produced. Mura defect is a kind of display defect on the liquid crystal display (LCD) screen. It has various forms, such as uneven brightness or color in some areas of the screen, low contrast between the defect area and the surrounding background, blurred edges, and so on. These characteristics make the traditional methods based on edge detection and threshold segmentation difficult to detect Mura defect effectively.
The traditional detection of Mura mainly depends on manual detection. So, this method has high labor cost, and the result of Mura detection is greatly influenced by the experience of workers. It clearly cannot meet the requirements of large batch orders of screen detection. Therefore, it is urgent to study the technology of automatic detection and location of Mura defect on the phone screen based on the computer. In recent years, many kinds of automatic detection methods are applied to detecting Mura defect. Due to the visibility of Mura defect on different display screens is different and various, a lot of parameters need to be adjusted from time to time during the whole detection process, leading relevant method wasting too much time if the number of screens is large.
This paper proposes a new method of detecting line Mura based on gray curve of the image and BP neural network. Firstly, the image is preprocessed to reduce the influence of noise. Then the gray curves on the sampling lines in the image are analyzed to find out the location of the gray discontinuous parts. Since Mura defect often appears in the discontinuous parts of the gray sampling line of the image, it can help judge whether Mura is present on the screen.
On this foundation, considering BP neural network is strongly nonlinear and with good robustness, the feature information reflecting whether Mura exists is extracted from the gray curves of the image for training. After training, the BP neural network can detect Mura defect automatically.
The experimental results show that, compared with the existing Mura detection methods, the improved method in this paper can distinguish line Mura defect on the mobile phone screen more accurately and quickly. The accuracy rate is up to 98.33%, and no parameter needs to be adjusted during the detection process, realizing automatic detection and positioning of line Mura.
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图 1 手机屏幕样本与缺陷所在位置。(a)横向Mura；(b)纵向Mura
Figure 1. Mobile screen samples and locations of Mura defect. (a) Horizontal Mura; (b) Vertical Mura
图 2 图像降采样前后采样线灰度曲线示意图。(a)降采样后采样线灰度曲线；(b)未降采样采样线灰度曲线
Figure 2. Gray curves of sampling line before and after downsampling. (a) Gray curves of sampling line after downsampling; (b) Gray curves of sampling line before downsampling
图 3 手机屏幕暗条纹线Mura对应关系示意图。(a)手机屏幕采样线示意图；(b)手机屏幕采样线灰度变化示意图
Figure 3. Connection between black line Mura and mobile screen. (a) Sampling lines on mobile screen; (b) Change of sampling lines on mobile screen
图 8 部分手机屏幕原图像。(a)本文算法未成功检测的屏幕图像；(b)人工漏检屏幕图像；(c)本文算法误检图像1；(d)本文算法误检图像2
Figure 8. Some original screen images. (a) The screen image not successfully detected by the algorithm proposed; (b) The screen image missed manually; (c) False check image 1; (d) False check image 2
表 1 线Mura检测率
Table 1. Detection rate of line Mura
序号 类别 样本个数 成功检测的样本数 检测成功率/% 1 线 Mura 120 119 99.17 2 无 Mura 60 53 88.33 
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参考文献
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