+1 Recommend
1 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Car detection and classification using cascade model


      , 1 , 1 , 1 , 1 , 2 , 3

      IET Intelligent Transport Systems

      The Institution of Engineering and Technology

      image classification, learning (artificial intelligence), neural nets, pattern classification, feature extraction, object detection, computer vision, traffic engineering computing, discriminative object parts, key information, authors, traditional convolutional neural network models, confidence score, predicted box, traditional approaches, fine-grained classification, bounding box predictors, network training, original images, cascade methods, cascade model, vision-based classification methods, vehicle-type classification, real-world image, intelligent transportation system, car appearance

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          In recent years, a number of vision-based classification methods have been proposed. However, a few of them were paid attention to vehicle-type classification in a real-world image, which is an important part of the intelligent transportation system. Owing to the large variances of the car appearance in images, it is critical to capture the discriminative object parts that can provide key information about the car pose. In the authors’ project, the traditional convolutional neural network (CNN) models are modified and experiments are followed as well. The model has two main contributions. First, the output shows a confidence score of how likely this box contains a car for each predicted box, which has some certain advantages compared with other models and is quite different from traditional approaches. Another contribution is the fine-grained classification of the makers and models of a car, which need to train the bounding box predictors as part of the network training. The experiment results show that data enhancement and pre-train of CNNs with original images can classify the vehicle makes and models with a high accuracy of nearly 80%. Cropping images by cascade methods can increase the precision to 86.6%.

          Related collections

          Most cited references 14

          • Record: found
          • Abstract: not found
          • Article: not found

          Very deep convolutional networks for large-scale image recognition.

            • Record: found
            • Abstract: not found
            • Article: not found

            ImageNet classification with deep convolutional neural networks

              • Record: found
              • Abstract: not found
              • Article: not found

              Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data


                Author and article information

                IET Intelligent Transport Systems
                IET Intell. Transp. Syst.
                The Institution of Engineering and Technology
                30 July 2018
                22 August 2018
                December 2018
                : 12
                : 10
                : 1201-1209
                [1 ] Department of Automotive and Transportation Engineering, Automotive Engineering Research Institute, Jiangsu University , Zhenjiang 212013, People's Republic of China
                [2 ] School of Computer Science and Communication Engineering, Jiangsu University , Zhenjiang 212013, People's Republic of China
                [3 ] Department of Mechanical Engineering, Shizuoka Institute of Science and Technology , Shizuoka 437-8555, Japan
                IET-ITS.2018.5270 ITS.2018.5270

                This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License ( http://creativecommons.org/licenses/by-nc-nd/3.0/)

                Page count
                Pages: 0
                Research Article


                Comment on this article