Vehicle Detection and Classification Using Convolutional Neural Networks

The vehicle detection and classification are important tasks in intelligent transportation system. The traditional methods of vehicle detection and classification often cause the coarse-grained results due to suffering from the limited viewpoints. Inspired by the latest achievements of Deep Learning successfully applied on images classification in recent years, this paper presents a method based on convolutional neural network, which consists of two steps: vehicle area detection and vehicle brand classification. Several typical network models have been applied in training and classification experiments for the detailed contrast analysis, such as RCNN (Regions with Convolutional Neural Network features), Faster RCNN, AlexNet, Vggnet, GoogLenet and Resnet. The proposed method can identify the vehicle models, brands and other information accurately, with the original dataset and enriched dataset, the algorithm can obtain the results with average accuracy about 93.32% in the classification of six kinds of vehicle models.