Maiga,B.Dalveren,Y.Kara,A.Derawi,M.Department of Electrical & Electronics Engineering2024-07-052024-07-05202312071-105010.3390/su1523162922-s2.0-85188720986https://doi.org/10.3390/su152316292Kara, Ali/0000-0002-9739-7619; Dalveren, Yaser/0000-0002-9459-0042Vehicle 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. © 2023 by the authors.eninfo:eu-repo/semantics/openAccessbad weatherdeep learningintelligent transportation systemtiny imagesArticleQ2Q21523WOS:001116187600001