Open AccessPavement Crack Segmentation using a U-Net based Neural Network
Author(s) -
Raido Lacorte Galina,
Thadeu Pezzin Melo,
Karin Satie Komati
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2021.18893
Subject(s) - artificial neural network , computer science , task (project management) , segmentation , artificial intelligence , net (polyhedron) , pattern recognition (psychology) , engineering , mathematics , geometry , systems engineering
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%.