Automated detection of fault types in CCTV sewer surveys

Sewers must be regularly inspected to prioritise effective maintenance, which can be an expensive and time-consuming process. This paper presents a methodology to automatically identify the type of a detected fault using raw closed circuit television (CCTV) footage. The procedure calculates the GIST descriptor of a video frame containing a fault before applying a collection of random forest classifiers to identify the fault’s type. Order oblivious filtering is used to further improve the methodology’s performance on continuous footage. The technology, including various classifier architectures, has been validated and demonstrated on CCTV footage collected by Wessex Water. The methodology achieved a peak accuracy of 73% when applied to well-represented fault types, showing promise for future application in the water industry. doi: 10.2166/hydro.2018.073 s://iwaponline.com/jh/article-pdf/21/1/153/517459/jh0210153.pdf Joshua Myrans (corresponding author) Centre for Water Systems, University of Exeter, Harrison Building, North Park Road, Exeter, Devon, UK E-mail: jm494@exeter.ac.uk Richard Everson Department of Computer Science, University of Exeter, Harrison Building, North Park Road, Exeter, Devon, UK Zoran Kapelan Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Delft, The Netherlands