eprintid: 10051437
rev_number: 24
eprint_status: archive
userid: 608
dir: disk0/10/05/14/37
datestamp: 2018-07-03 14:59:41
lastmod: 2021-09-26 22:59:45
status_changed: 2018-07-03 14:59:41
type: proceedings_section
metadata_visibility: show
creators_name: Panella, F
creators_name: Boehm, J
creators_name: Loo, Y
creators_name: Kaushik, A
creators_name: Gonzalez, D
title: Deep learning and image processing for automated crack detection and defect measurement in underground structures
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F44
keywords: Deep Learning, Automated Crack Detection, Photographic Tunnelling Surveys
note: © Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
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.
date: 2018-05-30
date_type: published
publisher: ISPRS SC
official_url: https://doi.org/10.5194/isprs-archives-XLII-2-829-2018, 2018
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1563531
doi: 10.5194/isprs-archives-XLII-2-829-2018
lyricists_name: Boehm, Jan
lyricists_name: Panella, Fabio
lyricists_id: JOBOE65
lyricists_id: FPANE86
actors_name: Bracey, Alan
actors_id: ABBRA90
actors_role: owner
full_text_status: public
publication: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
volume: XLII-2
number: 2
place_of_pub: Riva del Garda, Italy
pagerange: 829-835
event_title: ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”
issn: 1682-1750
book_title: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
citation:        Panella, F;    Boehm, J;    Loo, Y;    Kaushik, A;    Gonzalez, D;      (2018)    Deep learning and image processing for automated crack detection and defect measurement in underground structures.                     In:  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.  (pp. pp. 829-835).  ISPRS SC: Riva del Garda, Italy.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10051437/1/isprs-archives-XLII-2-829-2018.pdf