Real-time fuel truck detection algorithm based on deep convolutional neural network

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dc.contributor.authorAlsanad, Hamid R.
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorİlyas, Muhammad
dc.contributor.authorKhan, Atta Ur Rehman
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T11:34:00Z
dc.date.available2021-05-15T11:34:00Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionAlsanad, Hamid R./0000-0003-3433-9144; Khan, Atta ur Rehman/0000-0003-2930-6508
dc.description.abstractThis paper presents a new approach by training and improving a convolutional neural network (CNN) based on You Only Look Once version 2 (YOLOv2) to efficiently detect fuel trucks from images in embedded systems. The proposed method considers the entire image area for strong object detection compared with existing methods that only focus on the image area where the class object exists to predict its probability to be in a class. The loss function for CNN is improved to enhance effective learning, especially when only a limited amount of data is available for training. The class probability can be learned by improving the loss function although the anchor boxes are not in the center of the target object. The learning process of the model can be in a limited range and achieve rapid convergence although the sizes of the initial anchor and target boundary boxes are different. Experimental results of various fuel truck images show the efficiency of the proposed approach under different detection scenarios of real fuel trucks. The detection rate of the proposed method is approximately 4% higher than the YOLOv2 object detection method. The proposed method is suitable to monitor long country borders using unmanned drones.en_US
dc.identifier.doi10.1109/ACCESS.2020.3005391
dc.identifier.endpage118817en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85088048155
dc.identifier.scopusqualityQ1
dc.identifier.startpage118808en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3005391
dc.identifier.urihttps://hdl.handle.net/20.500.12939/265
dc.identifier.volume8en_US
dc.identifier.wosWOS:000551794900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlsanad, Hamid R.
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorİlyas, Muhammad
dc.institutionauthorBayat, Oğuz
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectCNNen_US
dc.subjectObject Detectionen_US
dc.subjectFuel Trucksen_US
dc.subjectYou Only Look Onceen_US
dc.subjectYOLOv2en_US
dc.titleReal-time fuel truck detection algorithm based on deep convolutional neural network
dc.typeArticle

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