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

Tennakoon, R, Hoseinnezhad, R, Tran, H and Bab-Hadiashar, A 2018, 'Visual Inspection of Storm-Water Pipe Systems Using Deep Convolutional Neural Networks', in Kurosh Madani and Oleg Gusikhin (ed.) Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2018), Porto, Portugal, 29-31 July 2018, pp. 135-140.


Document type: Conference Paper
Collection: Conference Papers

Title Visual Inspection of Storm-Water Pipe Systems Using Deep Convolutional Neural Networks
Author(s) Tennakoon, R
Hoseinnezhad, R
Tran, H
Bab-Hadiashar, A
Year 2018
Conference name ICINCO 2018: Volume 1
Conference location Porto, Portugal
Conference dates 29-31 July 2018
Proceedings title Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2018)
Editor(s) Kurosh Madani and Oleg Gusikhin
Publisher SciTePress
Place of publication Setubal, Portugal
Start page 135
End page 140
Total pages 6
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.
Subjects Computer Vision
Keyword(s) Storm-Water Pipe Inspection
Automated Infrastructure Inspection
Deep Convolutional Neural Networks
DOI - identifier 10.5220/0006851001450150
Copyright notice Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
ISBN 9789897583216
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