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Vehicle Type Recognition Based on Data Augmentation and Deep Learning

The task of vehicle type recognition in images is mainly used in parking lots, road and bridge toll management and public security traffic management departments. However, in practical applications, the recognition method has low recognition accuracy due to factors such as background clutter, illumination changes, diverse viewing angles, partial occlusion, and motion blur. In order to solve this problem, this paper adopts the deep learning method to realize the task of vehicle recognition. Due to the limited amount of image data, the training of deep learning cannot be satisfied. To solve this problem, this paper adopts the data augmentation (DA) method to expand the scale of the data set can realize intelligent vehicle recognition based on deep learning. The experimental results show that the method proposed in this paper is superior to the existing vehicle recognition methods based on deep learning, and the recognition accuracy can reach 92%.