Pauly, L, Hogg, D, Fuentes, R orcid.org/0000-0001-8617-7381 et al. (1 more author) (2017) Deeper Networks for Pavement Crack Detection. In: Proceedings of the 34th ISARC. 34th International Symposium in Automation and Robotics in Construction, 28 Jun - 01 Jul 2017, Taipei, Taiwan. IAARC , pp. 479-485.
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © This is an author produced version of a paper published in 34th International Symposium on Automation and Robotics in Construction. |
Keywords: | Pavement cracks, Detection, Deep learning, Convolutional neural networks |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) > Institute for Resilient Infrastructure (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Aug 2017 11:26 |
Last Modified: | 13 Jun 2022 13:15 |
Status: | Published |
Publisher: | IAARC |
Identification Number: | 10.22260/isarc2017/0066 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:120380 |