UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Rapid object detection systems, utilising deep learning and unmanned aerial systems (UAS) for civil engineering applications

Griffiths, D; Boehm, J; (2018) Rapid object detection systems, utilising deep learning and unmanned aerial systems (UAS) for civil engineering applications. In: 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”. (pp. pp. 391-398). International Society for Photogrammetry and Remote Sensing (ISPRS) Green open access

[img]
Preview
Text
isprs-archives-XLII-2-391-2018.pdf - Published version

Download (6MB) | Preview

Abstract

With deep learning approaches now out-performing traditional image processing techniques for image understanding, this paper accesses the potential of rapid generation of Convolutional Neural Networks (CNNs) for applied engineering purposes. Three CNNs are trained on 275 UAS-derived and freely available online images for object detection of 3m2segments of railway track. These includes two models based on the Faster RCNN object detection algorithm (Resnet and Incpetion-Resnet) as well as the novel one-stage Focal Loss network architecture (Retinanet). Model performance was assessed with respect to three accuracy metrics. The first two consisted of Intersection over Union (IoU) with thresholds 0.5 and 0.1. The last assesses accuracy based on the proportion of track covered by object detection proposals against total track length. In under six hours of training (and two hours of manual labelling) the models detected 91.3%, 83.1% and 75.6% of track in the 500 test images acquired from the UAS survey Retinanet, Resnet and Inception-Resnet respectively. We then discuss the potential for such applications of such systems within the engineering field for a range of scenarios.

Type: Proceedings paper
Title: Rapid object detection systems, utilising deep learning and unmanned aerial systems (UAS) for civil engineering applications
Event: 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy
Open access status: An open access version is available from UCL Discovery
DOI: 10.5194/isprs-archives-XLII-2-391-2018
Publisher version: https://doi.org/10.5194/isprs-archives-XLII-2-391-...
Language: English
Additional information: Copyright © The Authors 2018. This work is distributed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Object detection, Deep Learning, Unmanned Aerial Systems, Railway, Rapid
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10051464
Downloads since deposit
141Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item