Pavement Crack Segmentation using a U-Net based Neural Network - Galina, Raido Lacorte; Melo, Thadeu Pezzin; Komati, Karin Satie (Anais do Workshop de Visão Computacional (WVC), 2021)
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 contraction 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%.