PIPE-CovNet+: A Hyper-Dense CNN for Improved Pipe Abnormality Detection

Wang, X; Thiyagarajan, K; Kodagoda, S; Sharma, C

PIPE-CovNet+: A Hyper-Dense CNN for Improved Pipe Abnormality Detection

Wang, X Thiyagarajan, K

Kodagoda, S

Sharma, C

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Sensors Letters, 2024, 8, (4), pp. 1-4
Issue Date:: 2024-04-01

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.contributor.author	Sharma, C
dc.date.accessioned	2024-08-07T05:06:08Z
dc.date.available	2024-08-07T05:06:08Z
dc.date.issued	2024-04-01
dc.identifier.citation	IEEE Sensors Letters, 2024, 8, (4), pp. 1-4
dc.identifier.issn	2475-1472
dc.identifier.issn	2475-1472
dc.identifier.uri	http://hdl.handle.net/10453/180270
dc.description.abstract	In this letter, we propose a PIPE-CovNet+ model that is ba-sed on convolutional neural networks (CNNs) with multiple kernel sizes, gradient boosting techniques, and hyper-densely connected layers for abnormality detection in wastewater pipe infrastructure. The PIPE-CovNet+ model achieved 85% accuracy and 84% F1-score following assessment utilizing an open-source dataset, overcoming the constraints of imbalanced data and over-fitting issues. Furthermore, it showed greater efficacy when compared against other relevant models, displaying 3% higher accuracy and F1-score than our prior work under ideal test settings. Installing PIPE-CovNet+ into a robot system can alleviate the limitations of being tedious, laborious, and prone to human error in closed-circuit television (CCTV) inspections.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Letters
dc.relation.isbasedon	10.1109/LSENS.2024.3374814
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	PIPE-CovNet+: A Hyper-Dense CNN for Improved Pipe Abnormality Detection
dc.type	Journal Article
utslib.citation.volume	8
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - RI - Robotics Institute
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:06:06Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	4

Abstract:

In this letter, we propose a PIPE-CovNet+ model that is ba-sed on convolutional neural networks (CNNs) with multiple kernel sizes, gradient boosting techniques, and hyper-densely connected layers for abnormality detection in wastewater pipe infrastructure. The PIPE-CovNet+ model achieved 85% accuracy and 84% F1-score following assessment utilizing an open-source dataset, overcoming the constraints of imbalanced data and over-fitting issues. Furthermore, it showed greater efficacy when compared against other relevant models, displaying 3% higher accuracy and F1-score than our prior work under ideal test settings. Installing PIPE-CovNet+ into a robot system can alleviate the limitations of being tedious, laborious, and prone to human error in closed-circuit television (CCTV) inspections.

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http://hdl.handle.net/10453/180270