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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.
Mobile screen samples and locations of Mura defect. (a) Horizontal Mura; (b) Vertical Mura
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
Connection between black line Mura and mobile screen. (a) Sampling lines on mobile screen; (b) Change of sampling lines on mobile screen
Locations of Isolated points and extreme points
Flowchart of detecting line Mura
Input value of BP neural network
Mean square error curve of BP network
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
Comparison of results of different algorithms. (a) Original image (enhanced contrast); (b) Detection results of the proposed method; (c) Detection results of Ref. [9]; (d) Detection results of Ref. [10]