Pavement Crack Segmentation using a U-Net based Neural Network

Raido Lacorte Galina, Thadeu Pezzin Melo, Karin Satie Komati
2021 Anais do XVII Workshop de Visão Computacional (WVC 2021)   unpublished
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
more » ... 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%.
doi:10.5753/wvc.2021.18893 fatcat:u7dgdikpjzhh5l4k75xgskh74e