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Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices

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dc.contributor.authorMaiga, Bamoye
dc.contributor.authorDalveren, Yaser
dc.contributor.authorKara, Ali
dc.contributor.authorDerawi, Mohammad
dc.date.accessioned2023-12-21T10:24:10Z
dc.date.available2023-12-21T10:24:10Z
dc.date.issued2023-11-24
dc.descriptionOpen Access, Published by Sustainability, https://doi.org/10.3390/su152316292, Bamoye Maiga, Graduate School of Natural and Applied Sciences, Department of Electrical and Electronics Engineering, Atilim University, Ankara 06830, Turkey, Yaser Dalveren, Department of Electrical and Electronics Engineering, Atilim University, Ankara 06830, Turkey, Ali Kara, Department of Electrical and Electronics Engineering, Gazi University, Ankara 06570, Turkey, Mohammad Derawi, Department of Electronic Systems, Norwegian University of Science and Technology, 2815 Gjovik, Norway.
dc.description.abstractVehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture.
dc.identifier.citationhttp://hdl.handle.net/20.500.14411/1923
dc.identifier.issn2071-1050
dc.identifier.urihttps://doi.org/10.3390/su152316292
dc.language.isoen
dc.publisherSustainability
dc.relation.ispartofseries15; 23
dc.subjectBad weather; deep learning; intelligent transportation system; tiny images
dc.titleConvolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices
dc.typeArticle
dspace.entity.typeArticle

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