TFT‐LCD mura defect visual inspection method in multiple backgrounds

Abstract

The defect of mura of thin film transistor liquid crystal display (TFT‐LCD) panel is small, and the gray change is unknown. In manual detection, mura is easily overlooked, which is time‐consuming and labor‐intensive. These factors resulted in our inability to properly assess and distinguish multiple mura defects on a single image in field inspections. Aiming at the above problems, this article proposes a multibackground TFT‐LCD mura defect visual inspection method. To obtain the optimal algorithm under high‐precision and high‐speed detection and to overcome the problem of missed detection of the YOLOV4‐tiny algorithm, the spatial pyramid pooling module and squeeze‐and‐excitation module are added on the basis of the original framework. On the one hand, the advantage of the improved algorithm in this article is to focus on the key information, so that the information before feature fusion is more abundant. On the other hand, adding spatial pyramid pooling in BackBone enables the network to perform multiscale pooling and fusion on the input feature layer, which greatly enhances the receptive field of the network and enables the network to extract richer feature information. Experimental results show that the improved YOLOV4‐tiny algorithm has an accuracy of 99.72% in TFT‐LCD mura detection, which is 0.99% higher than YOLOV4‐tiny, and the real‐time detection speed reaches 63.84 frames per second (FPS). At the same time, we overcome the problem of missed detection of YOLOV4‐tiny algorithm and meet the detection accuracy and real‐time requirements of TFT‐LCD mura detection tasks in multibackground environment.

Color Mura Defect Detection Method Based on Channel Contrast Sensitivity Function Filtering

“…In the field of machine vision-based defect detection, extensive research has been conducted on Mura defect detection. The methods for detecting Mura defects can be broadly categorized into two main approaches: background reconstruction-based methods [3][4][5][6][7] and deep learning techniques [8][9][10][11][12][13]. Methods based on image background reconstruction are more common.…”

“…Chang et al [12] proposed a convolutional neural networkbased multi-classification model for micro defects in TFT-LCD, enabling the classification of defective pixels on the display panel. Chen et al [13] improved YOLOv4 [18] by adding spatial pyramid pooling modules and squeeze-and-excitation modules, increasing the network's receptive field and improving defect detection accuracy. Lin et al [19] proposed using a deep channel attention classification network as a feature extractor, combined with the adversarial training algorithm of convolutional neural networks, to achieve Mura defect detection under a few-shot scenario.…”

P‐7.21: Novel Type of Mask Compensation Improves Incomplete Exposure Mura

“…In recent years, as the display screen industry has entered a stage of survival of the fittest, consumers are increasingly demanding on display quality and clarity [1]. And the pursuit of ultimate costeffectiveness has become mainstream.…”