Deeper networks for pavement crack detection
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. Even though deep learning has been used before for crack detection, the network used is rather shallow when compared to the current networks used for other applications. In this paper we demonstrate the effectiveness of using deeper networks in computer vision based pavement crack detection for improved accuracy. We also show how variations in location of training and testing datasets affect the performance of the deep learning based pavement crack detection method.
Leo Pauly, Harriet Peel, Shan Luo, David Hogg, Raul Fuentes (2017). Deeper networks for pavement crack detection. Proceedings of the 34th International Symposium on Automation and Robotics in Construction (IAARC, 2017). 663–670. Read publication.
