Mr. Shengsen Zhang Profile

Shengsen Zhang

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Proceedings Article | 2 December 2020 Paper

A band-shaped Mura detection method based on unsupervised deep learning

ShuBin Song, ZengQiang Zheng, ShengSen Zhang, WeiFei Ma, Min Xia

Proceedings Volume 11717, 117170R (2020) https://doi.org/10.1117/12.2585259

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Mura is a phenomenon in which the displays have various uneven display defects. The band-shaped Mura has the characteristics of irregular shape and different sizes. And the new shapes and sizes of Mura may appear at any time during the inspection process. Therefore, traditional image algorithms are difficult to detect the band-shaped Mura anomaly. In response to the above problems, this paper proposes the Res-unetGAN network, which is an unsupervised anomaly detection method based on generative adversarial network. We design resnet50 as the encoding network of the generator to obtain the latent feature vectors. To improve the quality of reconstructed samples, we combine the skipconnection structure into the generator to guide the decoder. The discriminator is a convolutional neural network based on the Depthwise Separable Convolution. The purpose is to distinguish between normal samples and reconstructed samples, and form a game process with the generator. The network only needs normal screen samples during the training process. In the test, since the Mura sample has not been trained, the reconstruction error score of the Mura sample will be higher. After repeated experiments on the band-shaped Mura data set, the highest auc of 0.995 was obtained, which is better than several models for comparison.

Proceedings Article | 2 December 2020 Paper

A band Mura detection method based on a new generative adversarial network

Chen Xie, Zengqiang Zheng, Shengsen Zhang, Chunxu Chen, Wei Li

Proceedings Volume 11717, 117170C (2020) https://doi.org/10.1117/12.2584212

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Defined as a visual inhomogeneity, Mura can cause seriously unpleasant feelings and that’s why it needs to be inspected. Band Mura, which has a large area, is particularly difficult to be detected because of its irregular shape and size as well as its low contrast. So we propose a UADD-GAN model to detect band Mura in this work. Consisting of a proposed UADD generator and a discriminator, the model is trained with some normal samples, after which the generator is able to simulate the distribution of normal samples. During training, the generator takes normal images as inputs and output their reconstructions, while the discriminator receives images and determines whether they’re true or fake, defiantly helping the generator to perform reconstructions better. The symmetric structure and operation of skipadding make it easy for the UADD generator to reconstruct the normal samples well. On the contrary, the generator performs worse in the reconstruction of Mura samples so that we can distinguish them from the normal ones. In addition, we use multiple feature layers of the discriminator for supervision instead of using only the classification layer, helping the generator to reconstruct normal samples better. We’ve conducted experiments of MURA data sets with different proportion and our research indicates that the proposed model surpass other state of the art methods.

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