CNN Architectures for Vehicle Detection in V2X Environments: A Comparative Study
Vasileios Efthymiou, Stylianos Basagiannis, Sophia G. Petridou · 2024
Leveraging convolutional neural networks (CNNs) to Vehicle-to-Everything (V2X) communication systems offers a promising approach to vehicle detection, which is essential for improving road safety and traffic efficiency. At the same time, parameterization of CNNs’ architectures in order to achieve high accuracy in moving objects’ detection becomes an engineering challenge, especially when data pre- and post-processing solutions need to respect timing requirements. Our study evaluates the performance of five CNN architectures, namely YOLO in v5, v8, and v9, Faster R-CNN, and VGG16, for car detection. We utilize an open dataset including images derived from road-traffic environments and assess the models’ performance using benchmark evaluation metrics, i.e., precision, recall, mean Average Precision (mAP50, mAP50 – 95), and F1-score. Furthermore, we analyze their computational requirements, in terms of execution time and resource demands, which are critical factors when considering real-time applications in V2X systems, such as autonomous driving and traffic management. Our evaluation results indicate that, while the YOLO architecture exhibits the best balance of speed and accuracy, Faster R-CNN has higher detection performance at the cost of greater computational demand. This paper provides insights into each model’s strengths and limitations offering guidance for selecting optimal architectures tailored to V2X-related applications.