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

Bamoye Maiga, Yaser Dalveren, Ali Kara and Mohammad Derawi ()
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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

Sustainability, 2023, vol. 15, issue 23, 1-14

Abstract: Vehicle 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.

Keywords: bad weather; deep learning; intelligent transportation system; tiny images (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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