Visual Inspection of Storm-Water Pipe Systems using Deep Convolutional Neural Networks

Ruwan Tennakoon, Reza Hoseinnezhad, Huu Tran, Alireza Bab-Hadiashar

2018

Abstract

Condition monitoring of storm-water pipe systems are carried-out regularly using semi-automated processors. Semi-automated inspection is time consuming, expensive and produces varying and relatively unreliable results due to operators fatigue and novicity. This paper propose an innovative method to automate the storm-water pipe inspection and condition assessment process which employs a computer vision algorithm based on deep-neural network architecture to classify the defect types automatically. With the proposed method, the operator only needs to guide the robot through each pipe and no longer needs to be an expert. The results obtained on a CCTV video dataset of storm-water pipes shows that the deep neural network architectures trained with data augmentation and transfer learning is capable of achieving high accuracies in identifying the defect types.

Paper Citation

in Harvard Style

Tennakoon R. (2018). Visual Inspection of Storm-Water Pipe Systems using Deep Convolutional Neural Networks.In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-321-6, pages 135-140. DOI: 10.5220/0006851001350140

in Bibtex Style

@conference{icinco18,
author={Ruwan Tennakoon},
title={Visual Inspection of Storm-Water Pipe Systems using Deep Convolutional Neural Networks},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2018},
pages={135-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006851001350140},
isbn={978-989-758-321-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Visual Inspection of Storm-Water Pipe Systems using Deep Convolutional Neural Networks
SN - 978-989-758-321-6
AU - Tennakoon R.
PY - 2018
SP - 135
EP - 140
DO - 10.5220/0006851001350140