An Accurate Mura Defect Vision Inspection Method Using Outlier-Prejudging-Based Image Background Construction and Region-Gradient-Based Level Set

Abstract

The visual inspection of Mura defects is still a challenging task in the quality control of panel displays because of the intrinsically nonuniform brightness and blurry contours of these defects. The current methods cannot detect all Mura defect types simultaneously, especially small defects. In this paper, we introduce an accurate Mura defect visual inspection (AMVI) method for the fast simultaneous inspection of various Mura defect types. The method consists of two parts: an outlier-prejudging-based image background construction (OPBC) algorithm is proposed to quickly reduce the influence of image backgrounds with uneven brightness and to coarsely estimate the candidate regions of Mura defects. Then, a novel region-gradient-based level set (RGLS) algorithm is applied only to these candidate regions to quickly and accurately segment the contours of the Mura defects. To demonstrate the performance of AMVI, several experiments are conducted to compare AMVI with other popular visual inspection methods are conducted. The experimental results show that AMVI tends to achieve better inspection performance and can quickly and accurately inspect a greater number of Mura defect types, especially for small and large Mura defects with uneven backlight. Note to Practitioners —The traditional Mura visual inspection method can address only medium-sized Mura defects, such as region Mura, cluster Mura, and vertical-band Mura, and is not suitable for small Mura defects, for example, spot Mura. The proposed accurate Mura defect visual inspection (AMVI) method can accurately and simultaneously inspect not only medium-sized Mura defects but also small and large Mura defects. The proposed outlier-prejudging-based image background construction (OPBC) algorithm of the AMVI method is employed to improve the Mura true detection rate, while the proposed region-gradient-based level set (RGLS) algorithm is used to reduce the Mura false detection rate. Moreover, this method can be applied to online vision inspection: OPBC can be implemented in parallel processing units, while RGLS is applied only to the candidate regions of the inspected image. In addition, AMVI can be extended to other low-contrast defect vision inspection tasks, such as the inspection of glass, steel strips, and ceramic tiles.