A vehicle recognition algorithm based on deep convolution neural network

Abstract

With the continuous development of image processing, pattern recognition, and computer vision, the image recognition technology based on deep learning (DL) has gradually entered the field of traffic management. In this paper, the DL theory is applied to vehicle recognition, and a vehicle recognition algorithm is constructed based on deep convolution neural network (DCNN). Specifically, the forward and back propagation algorithms of the DL were adopted to minimize the loss function, and the weights were updated via back propagation to obtain the recognition algorithm, which classifies and recognizes the input images. Experimental results show that the proposed algorithm is more accurate than the traditional CNN in vehicle image classification. The research results shed light on the application of the DL in intelligent transportation.