Li Y N, Zeng Q H, Zhang Y Y, et al. Mura detection and positioning in picture based on BP neural network[J]. Opto-Electron Eng, 2020, 47(11): 190725. doi: 10.12086/oee.2020.190725
Citation: Li Y N, Zeng Q H, Zhang Y Y, et al. Mura detection and positioning in picture based on BP neural network[J]. Opto-Electron Eng, 2020, 47(11): 190725. doi: 10.12086/oee.2020.190725

Mura detection and positioning in picture based on BP neural network

    Fund Project: Supported by the National Natural Science Foundation of China (61533008, 61374115, 61603181), the Fundamental Research Funds for the Central Universities (NJ20170005, NJ20170010), and the Priority Academic Program Development of Jiangsu Higher Education Institutions
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  • 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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  • 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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