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dc.contributor.authorPatil, Ravindra Rajaram
dc.contributor.authorCalay, Rajnish K
dc.contributor.authorMustafa, Mohamad
dc.contributor.authorAnsari, Saniya
dc.date.accessioned2023-08-29T12:01:36Z
dc.date.available2023-08-29T12:01:36Z
dc.date.issued2023-08-26
dc.description.abstractIn artificial intelligence (AI), computer vision consists of intelligent models to interpret and recognize the visual world, similar to human vision. This technology relies on a synergy of extensive data and human expertise, meticulously structured to yield accurate results. Tackling the intricate task of locating and resolving blockages within sewer systems is a significant challenge due to their diverse nature and lack of robust technique. This research utilizes the previously introduced “S-BIRD” dataset, a collection of frames depicting sewer blockages, as the foundational training data for a deep neural network model. To enhance the model’s performance and attain optimal results, transfer learning and fine-tuning techniques are strategically implemented on the YOLOv5 architecture, using the corresponding dataset. The outcomes of the trained model exhibit a remarkable accuracy rate in sewer blockage detection, thereby boosting the reliability and efficacy of the associated robotic framework for proficient removal of various blockages. Particularly noteworthy is the achieved mean average precision (mAP) score of 96.30% at a confidence threshold of 0.5, maintaining a consistently high-performance level of 79.20% across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95. It is expected that this work contributes to advancing the applications of AI-driven solutions for modern urban sanitation systems.en_US
dc.identifier.citationPatil Ravindra R Patil, Calay RK, Mustafa , Ansari S. AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems. Electronics. 2023;12(17)en_US
dc.identifier.cristinIDFRIDAID 2170156
dc.identifier.doihttps://doi.org/10.3390/electronics12173606
dc.identifier.issn2079-9292
dc.identifier.urihttps://hdl.handle.net/10037/30516
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.journalElectronics
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.titleAI-Driven High-Precision Model for Blockage Detection in Urban Wastewater 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)