Real-Time Vehicle Detection for Surveillance of River Dredging Areas Using Convolutional Neural Networks

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dc.contributor.authorAl, Bayati, M.A.Z.
dc.contributor.authorÇakmak, M.
dc.date.accessioned2024-09-29T16:16:01Z
dc.date.available2024-09-29T16:16:01Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe presence of illegal activities such as illegitimate mining and sand theft in river dredging areas leads to economic losses. However, manual monitoring is expensive and time-consuming. Therefore, automated surveillance systems are preferred to mitigate such activities, as they are accurate and available at all times. In order to monitor river dredging areas, two essential steps for surveillance are vehicle detection and license plate recognition. Most current frameworks for vehicle detection employ plain feed-forward Convolutional Neural Networks (CNNs) as backbone architectures. However, these are scale-sensitive and cannot handle variations in vehicles' scales in consecutive video frames. To address these issues, Scale Invariant Hybrid Convolutional Neural Network (SIH-CNN) architecture is proposed for real-time vehicle detection in this study. The publicly available benchmark UA-DETRAC is used to validate the performance of the proposed architecture. Results show that the proposed SIH-CNN model achieved a mean average precision (mAP) of 77.76% on the UA-DETRAC benchmark, which is 3.94% higher than the baseline detector with real-time performance of 48.4 frames per seconds. © 2023, Modern Education and Computer Science Press. All rights reserved.en_US
dc.identifier.doi10.5815/ijigsp.2023.05.02
dc.identifier.endpage28en_US
dc.identifier.issn2074-9082
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85175440565en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage17en_US
dc.identifier.urihttps://doi.org/10.5815/ijigsp.2023.05.02
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8791
dc.identifier.volume15en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherModern Education and Computer Science Pressen_US
dc.relation.ispartofInternational Journal of Image, Graphics and Signal Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAutomated surveillanceen_US
dc.subjectCNNen_US
dc.subjectRiver dredgingen_US
dc.subjectScale invarianten_US
dc.subjectVehicle detectionen_US
dc.titleReal-Time Vehicle Detection for Surveillance of River Dredging Areas Using Convolutional Neural Networksen_US
dc.typeArticleen_US

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