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Duculty, Free-form anisotropy: A new method for crack detection on pavement surface images, in 2011 18th IEEE International Conference on Image Processing, IEEE, (2011), 1069–1072. Marcos, Adaptive road crack detection system by pavement classification, Sensors, 11 (2011), 9628–9657. Ritchie, A neural network-based methodology for pavement crack detection and classification, Transport. X, Novel approach to pavement image segmentation based on neighboring difference histogram method, in 2008 Congress on Image and Signal Processing, IEEE, (2008), 792–796. Road crack segmentation using an attention residual U-Net with generative adversarial learning. In particular, compared with U-Net, the mIoU of our proposed method is increased about 3%~17% compared with the three public datasets.Ĭitation: Xing Hu, Minghui Yao, Dawei Zhang. Qualitative and quantitative comparisons between the proposed method and the state-of-the-art methods show that the proposed method outperforms or is comparable to the state-of-the-art methods in both F1 score and precision. The main contributions of this work are as follows: 1) We introduce a CNN model as a discriminate network to realize adversarial learning to guide the training of the segmentation network, which is trained in a min-max way: the discrimination network is trained by maximizing the loss function, while the segmentation network is trained with the only gradient passed by the discrimination network and aim at minimizing the loss function, and finally an optimal segmentation network is obtained 2) We add the residual modular and the visual attention mechanism to U-Net, which makes the segmentation results more robust, refined and smooth 3) Extensive experiments are conducted on three public road crack datasets to evaluate the performance of our proposed model. Besides, we use an adversarial network consisting of convolutional layers as a discrimination network. We create a segmentation network by introducing a visual attention mechanism and residual module to a fully convolutional network(FCN) to capture richer local features and more global semantic features and get a better segment result. This paper proposed an end-to-end road crack segmentation model based on attention mechanism and deep FCN with generative adversarial learning. Affected applications might return different connectivity errors, but they will all have untrusted root certificate errors in common. These problems occur because of failed verification of end entity certificate. Various applications that use certificates and Public Key Infrastructure (PKI) might experience intermittent problems, such as connectivity errors, once or twice per day/week. This article illustrates only one of the possible causes of untrusted root CA certificate. 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