Cracks on the concrete surface are symptoms and precursors of structural degradation and hence must be identified and remedied. However, locating cracks is a time-consuming task that requires specialized professionals and special equipment. The use of neural networks for automatic crack detection emerges to assist in this task. This work proposes one U-Net based neural network to perform crack segmentation, trained with the Crack500 and DeepCrack datasets, separately. The U-Net used has seven

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... ntraction and seven expansion layers, which differs from the original architecture of four layers of each part. The IoU results obtained by the model trained with Crack500 was 71.03%, and by the model trained with DeepCrack was 86.38%.