@InProceedings{OliveiraMaBaSoFrVi:2022:FiCaRe,
               author = "Oliveira, Franklin Lazaro Santos de and Macena, Arianne Santos da 
                         and Barbosa, Ot{\'a}vio Azevedo de Carvalho Kamel and Souza, 
                         Wesley and Freitas, Nicksson Ckayo Arrais de and Vinuto, Tiago Da 
                         Silva",
          affiliation = "{Federal University of Pernambuco} and {Federal University of 
                         Pernambuco} and {Federal University of Pernambuco} and {Federal 
                         University of Pernambuco} and SiDi and SiDi",
                title = "Fine-grained cars recognition using deep convolutional neural 
                         networks",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "intelligent transportation systems, fine-grained classification, 
                         car recognition.",
             abstract = "Population growth and the high concentration of vehicles on urban 
                         roads have been negatively impacting urban mobility and the global 
                         environment, since the primary transportation modes occupy a lot 
                         of space on the streets and are one of the main polluting gas 
                         emitters. In this context of inefficient urban mobility and 
                         unsustainability, the Intelligent Transportation Systems (ITS) 
                         aims to solve or minimize urban traffic issues. ITS are also 
                         widely used in applications focused on traffic safety, such as 
                         vehicle recognition related to a traffic or law violation. For 
                         this task, the fine-grained vehicle classification technique is 
                         used mainly by advances in computer vision and deep learning. 
                         However, identifying vehicles by the model can be a problem 
                         because the same vehicle can be easily misclassified when observed 
                         from different perspectives, with different colors, or by similar 
                         models. Knowing these inherent issues from vehicle recognition 
                         tasks, Deep Convolutional Neural Networks (DCNNs) are commonly 
                         used due to their ability to extract features from images. In that 
                         regard, the goal of this paper is to evaluate some state of art 
                         DCNNs architectures, conducting experiments with three different 
                         datasets to identify which architectures have the best performance 
                         metrics in the refined car classification task within ITS 
                         context.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
                  doi = "10.1109/SIBGRAPI55357.2022.9991761",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991761",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47MHK5B",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47MHK5B",
           targetfile = "oliveira-20.pdf",
        urlaccessdate = "2024, Apr. 29"
}