车辆颜色和型号识别算法研究与应用

doi:10.12052/gdutxb.200051

Abstract

Abstract: In order to solve the problem of low recognition accuracy of current vehicle color and model recognition methods based on machine learning, a vehicle color and model recognition method based on convolutional neural network is proposed. The method uses YOLOv3(You Only LookOnce Version 3) algorithm in Darknet network to detect and locate the vehicle face, and then the vehicle color and model recognition algorithm based on convolutional neural network is used to identifythe vehicle color and model. This is a multi-attribute recognition method for vehicle, it is different from the recognition method of single vehicle attribute. On public traffic data collection of Peking University Vehicle Datasets experiment, the experimental results show that the vehicle color and model recognition accuracy of 93.75% at the same time, the recognition accuracy of vehicle color is 94.98%, the recognition accuracy of vehicle model attribute recognition is 98.38%, It is obviously better than the vehicle attribute recognition algorithm based on machine learning, the algorithm is proved to be feasible and effective. Finally, the vehicle color and model recognition technology is applied to the intelligent parking fee system.

Key words: vehicle color recognize, vehicle model recognize, vehicle face, convolutional neural networks(CNN), intelligent parking fee system

CLC Number:

TP391

Zhan Yin-wei, Zhu Bai-wan, Yang Zhuo. Research and Application of Vehicle Color and Model Recognition Algorithm[J].Journal of Guangdong University of Technology, 2020, 37(04): 9-14.

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