Publications / 2017 Proceedings of the 34rd ISARC, Taipei, Taiwan

Deeper Networks for Pavement Crack Detection

Leo Pauly, Harriet Peel, Shan Luo, David Hogg and Raul Fuentes
Pages 479-485 (2017 Proceedings of the 34rd ISARC, Taipei, Taiwan, ISBN 978-80-263-1371-7, ISSN 2413-5844)

Pavement crack detection using computer vision techniques has been studied widely over the past several years. However, these techniques have faced several limitations when applied to real world situations due to for example changes of lightning conditions or variation in textures. But the recent advancements in the field of artificial neural networks, especially in deep learning, have paved a new way for applying computer vision methods to pavement crack detection. In this paper we demonstrate the effectiveness of deep networks in computer vision based pavement crack detection. We also show how variations in location of training and testing datasets affect the performance of the deep learning based pavement crack detection method.

Keywords: Pavement cracks, Detection, Deep learning, Convolutional neural networks