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dc.contributor.authorPatil, Ravindra Rajaram
dc.contributor.authorMustafa, Mohamad
dc.contributor.authorCalay, Rajnish K
dc.contributor.authorAnsari, Saniya M.
dc.date.accessioned2023-08-30T10:49:34Z
dc.date.available2023-08-30T10:49:34Z
dc.date.issued2023-03-09
dc.description.abstractComputer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work.en_US
dc.identifier.citationPatil Ravindra R Patil, Mustafa , Calay RK, Ansari SM. S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems. Sensors. 2023;23(6)en_US
dc.identifier.cristinIDFRIDAID 2132911
dc.identifier.doi10.3390/s23062966
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10037/30546
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofPatil, R.R. (2024). Enhancing AI Systems through Representative Dataset, Transfer Learning, and Embedded Vision. (Doctoral thesis). <a href=https://hdl.handle.net/10037/32925>https://hdl.handle.net/10037/32925</a>.
dc.relation.journalSensors
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleS-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systemsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)