Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.<p> </p>
<span lang="EN-US">Intelligent transportation system (ITS) is currently one of the most discussed topics in scientific research. Actually, ITS offers advanced monitoring systems that include vehicle counting, pedestrian detection. Lately, convolutional neural networks (CNNs) are extensively used in computer vision tasks, including segmentation, classification, and detection. In fact, image semantic segmentation is a critical issue in computer vision applications. For example, self-driving vehicles require high accuracy with lower parameter requirements to segment the road scene objects in real-time. However, most related work focus on one side, accuracy or parameter requirements, which make CNN models difficult to use in real-time applications. In order to resolve this issue, we propose the efficient lightweight residual network (ELRNet), a novel and ELRNet, which is an asymmetrical encoder-decoder architecture. Indeed, in this network, we compare four varieties of the proposed factorized block, and three loss functions to get the best combination. In addition, the proposed model is trained from scratch using only 0.61M parameters. All experiments are evaluated on the popular public the cambridge-driving labeled video database (CamVid) road scene dataset and reached results show that ELRNet can achieve better performance in terms of parameters requirements and precision compared to related work.</span>
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