Color Point Defect Detection Method Based on Color Salient Features

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.