Deeper networks for pavement crack detection

Image of a city

Deeper networks for pavement crack detection

Jul 2 2017

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.

Perch & Repair icon

Perch & Repair

Perceive & Patch icon

Perceive & Patch

Fire & Forget icon

Fire & Forget

City & Society icon

City & Society

The Latest

Self Repairing Cities Logo

Get Involved

Find out more about the project, get in touch today.