Damage Assessment of the Built Infrastructure using Smartphones

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Title: Damage Assessment of the Built Infrastructure using Smartphones
Authors: O'Byrne, Michael
Pakrashi, Vikram
Schoefs, Franck
Ghosh, Bidisha
Permanent link: http://hdl.handle.net/10197/10338
Date: 30-Aug-2018
Online since: 2019-05-08T09:09:31Z
Abstract: The use of image-processing and machine-learning algorithms for road condition monitoring has attracted considerable interest in recent years. This surge in popularity has been propelled by advances in camera technology and the emergence of state-of-the-art deep learning techniques which have allowed inspectors to obtain high-quality imagery on a consistent basis, and then use efficient techniques to recognise road defects with credibility. A wide variety of road defect detection techniques have been proposed, however, the influence that different image acquisition devices have on the accuracy of defect detection has not been studied despite being a key component. The use of smartphone cameras as an inspection tool is of particular interest as they have become ubiquitous in recent times and the built-in cameras have progressed significantly. These cameras are now capable of producing perfectly acceptable images, yet they are still not well compared against established benchmarks. In this paper, the qualities of smartphones are explored and compared against a dedicated DSLR (Digital Single-Lens Reflex) camera. An experiment was designed that involved capturing video footage of a road surface using a smartphone (Samsung Galaxy S7) and a dedicated imaging device (Canon 600D DRSL). A deep learning based crack detection method was applied to imagery from the smartphone and the DRSL cameras and the performances levels were subsequently compared. The results indicate that smartphones are a viable, low-cost method for executing quick assessments of the road integrity. The evaluation of smartphone cameras also addresses the ongoing uncertainty around the level of performance that can be achieved using cheaper sensors for quantitative purposes. Such findings provide reassurance for inspectors wishing to use their smartphones for simple monitoring tasks.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: University College Dublin
Keywords: Image-processingRoad condition monitoringCrack detectionSmartphone sensors
Other versions: http://www.cerai.net/
Language: en
Status of Item: Not peer reviewed
Conference Details: Civil Engineering Research in Ireland 2018 (CERI2018), University College Dublin, Ireland, 29-30 August, 2018
Appears in Collections:Mechanical & Materials Engineering Research Collection

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