车辆颜色和型号识别算法研究与应用

Research and Application of Vehicle Color and Model Recognition Algorithm

针对目前基于机器学习的车辆颜色和型号识别方法的识别准确率低问题,提出基于卷积神经网络的车辆颜色和型号识别方法。该方法使用Darknet网络中YOLOv3(You Only LookOnce Version 3)算法对车辆图片的车脸进行检测与定位,再对车脸区域使用车辆颜色和型号识别算法同时识别车辆颜色和型号,这是对车辆多属性同时识别的方法,不同于车辆单一属性识别的方法。在公开车辆数据集(Peking University Vehicle Datasets,PKU-VD)上进行实验,实验结果表明,车辆颜色和型号同时识别准确率为93.75%,车辆颜色单一属性识别准确率为94.98%,车辆型号单一属性识别准确率98.38%,明显优于基于机器学习的车辆属性识别算法,从而验证该算法是可行且有效的。最后将车辆颜色和型号识别技术应用在智能停车场收费系统中。

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.