Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 829-835, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-829-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 829-835, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-829-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

DEEP LEARNING AND IMAGE PROCESSING FOR AUTOMATED CRACK DETECTION AND DEFECT MEASUREMENT IN UNDERGROUND STRUCTURES

F. Panella1,2, J. Boehm1, Y. Loo2, A. Kaushik2, and D. Gonzalez2 F. Panella et al.
  • 1Dept. of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK
  • 2Ove Arup & Partners Ltd., 13 Fitzroy Street, London W1T 4BQ, UK

Keywords: Deep Learning, Automated Crack Detection, Photographic Tunnelling Surveys

Abstract. This work presents the combination of Deep-Learning (DL) and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information). As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.