A Study Of Fine-Grained Vehicle Model Recognition Based On Deep Convolutional Neural Network

Fine-grained vehicle model recognition is an important research topic in the field of intelligent transportation systems.It also belongs to a task in computer vision.Fine-grained vehicle model recognition identifies the specific model of the vehicle from the vehicle picture or video.Different from the traditional vehicle recognition,that is,identifying the types of vehicles,such as cars,trucks,buses,etc.,fine-grained vehicle model recognition will identify a specific model of a specific manufacturer.The research on the traditional vehicle model recognition is mainly carried out through artificial design features and methods based on 3D models.The accuracy and generalization are poor,and it is difficult to apply to the actual intelligent transportation system.In recent years,deep learning,especially convolutional neural network,has a great impact on the artificial intelligence and is currently the dominant method for image recognition.It not only greatly improves the recognition accuracy but also reduces the complexity of the artificial design features.Now,the task of identifying fine-grained vehicle model has gradually begun to use convolutional neural networks.This paper proposes a detailed classification system based on the coarse-to-fine convolutional neural network architecture.In this system,we will use the global features and the finest local features of the models extracted from large-scale datasets for vehicle identification.In order to get the finest local features,we have proposed two important algorithms,one is positioning local area algorithm,and the other is from coarse-to-fine detection algorithm.For the local localization algorithm,we will establish a mapping relationship between the feature heatmap and the input data from the convolutional neural network,so as to extract the most discriminative information part in the input data according to the heat area in the heatmap.For coarse-to-fine detection algorithms,we will repeatedly use the local positioning region algorithm to extract the refined parts again,until no relevant region can be found in the heatmap.Finally,we combine the global features with the finest features,and used as the input of the SVM to identify vehicle models.The entire process does not include prior knowledge of human beings.The system automatically learns which part will help to identify models.This shows that our system is very robust and can be applied to other similar recognition tasks.Using this system,we achieved 98.29%accuracy on a large-scale dataset with 281 models,which have practical application value.