ResUnet-GAN with Dynamic Memory for Mura Defect Detection

'Mura' is a phenomenon in which panels have uneven display defects, irregular shapes, and different sizes. It is impossible to produce perfect panels on production lines, so panel inspection is necessary to differentiate between 'light Mura' and 'serious Mura' manually. The performance of conventional defect detection models for Mura detection is worse since they only differentiate between 'normal' and 'abnormal' samples. To reduce human cost and increase the accuracy of Mura detection, we propose a 'ResUnet-GAN with Dynamic Memory Model an unsupervised anomaly detection method based on a Generative Adversarial Network (GAN) with a memory module to distinguish panel defects. In the dynamic memory, we designed a dynamic feature filtering (DFF) method to choose important features of images, enhancing the ability to recognize light Mura features of the ResUnet-GAN. The proposed model can achieve an Area Under Curve (AUC) of approximately 0.8 for accurate Mura detection. The mechanism of this paper is novel, and the result contributes to practical application.