车辆颜色和型号识别算法研究与应用

战荫伟; 朱百万; 杨卓

doi:10.12052/gdutxb.200051

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车辆颜色和型号识别算法研究与应用

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- 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
- 广东工业大学学报 2020年37卷第4期页码：9-14
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  战荫伟(1966–)，男，教授，博士，主要研究方向为图像处理、人机交互、虚拟现实、增强现实
  朱百万(1985–)，男，硕士研究生，主要研究方向为图像处理和深度学习，E-mail：2428278928@qq.com
- 基金信息：
  
  广东省自然科学基金资助项目(2018A030313802)
- DOI：10.12052/gdutxb.200051
  中图分类号： TP391
- 网络首发：2020-07-11，
  
  纸质出版：2020
- 稿件说明：
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战荫伟, 朱百万, 杨卓. 车辆颜色和型号识别算法研究与应用[J]. 广东工业大学学报, 2020, 37(4): 9-14.

Zhan Yin-wei, Zhu Bai-wan, Yang Zhuo. Research and Application of Vehicle Color and Model Recognition Algorithm[J]. 2020, 37(4): 9-14.
战荫伟, 朱百万, 杨卓. 车辆颜色和型号识别算法研究与应用[J]. 广东工业大学学报, 2020, 37(4): 9-14. DOI： 10.12052/gdutxb.200051.

Zhan Yin-wei, Zhu Bai-wan, Yang Zhuo. Research and Application of Vehicle Color and Model Recognition Algorithm[J]. 2020, 37(4): 9-14. DOI： 10.12052/gdutxb.200051.

摘要

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。

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.

关键词

Keywords

references

邓柳. 基于深度卷积神经网络的车型识别[D]. 成都: 西南交通大学, 2015.

REDMON J, FARHADI A. YOLOv3: an incremental improvement[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE Computer Society, 2018: 89-95.

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//Proceedi ng of the 3rd International Conference on Learning Representations. SanDiego: ICLR, 2015: 1409-1556.

CHEN X, ZHANG J, CHEN W W, et al . Convolutional neural network algorithm and case [J ] . Journal of Guangdong University of Technology, 2017, 34(6): 20-26.

KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks[C]//26th Annual Conference on Neural Information Processing Systems. Lake Tahoe: Neur al Information Processing System Foundation, 2012: 1097-1105.

HEK, ZHANG X, REN S, et al. Deep residual learning for imagerecognition[C]//2016 IEEE Conference on Compute rVision and Pattern Recognition. Las Vegas: IEEE Computer Society, 2016: 770-778.

SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolueons[C]//2015 IEEE Conference on Computer Visi onand Pattern Recognition. Boston: IEEE Computer Society, 2015: 1-9.

HowarD A G, ZHU M, CHEN B, et al . Mobile Nets: efficient convolutional neural networks for mobile vision applications [J ] . arXiv preprint, 2017, 32(9): 100-108.

YIN H P, CHEN B, CHAI Y, et al . Vision-based object detection and tracking: a review [J ] . Acta Automatica Sinica, 2016, 42(10): 1466-1489.

LI X D, YE M, LI T. Review of object detection based on convolutional neural networks [J]. Application of Computer Research, 2017, 34(10): 2881-2886.

LAOPRACHA N, SUNAT K. Comparative study of computational time that HOG-Based features used forvehicle detection[C]//2017 International Conference on Computing and Information Technology. Helsinki: IEEE Computer Society, 2017: 275-284.

El-KHOREBY MOHAMED, ABU-BAKAR SYED. Vehicle detection and counting for complex weather conditions[C]//2017 IEEE International Conference on Signal and Image Processing Applications. Beijing: IEEE Computer Society, 2017: 425-428.

MAQBOOL S, KHAN M, TAHIR J, et al. Vehicle Detection, Tracking and Counting[C]// 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP). Shenzhen: IEEE, 2018: 126-132.

ZHANG H B, LI H L, HUANG X T, et al . Research and implementation of vehicle-type recognition method based on HOG features of vehicle frontal face [J ] . Computer Simulation, 2015, 32(12): 119-123.

PAN C, SUN M, YANZ. The study on vehicle detection based on DPM in traffic scenes[C]//2016 International Conference on Frontier Computing. Tokyo: FC, 2016: 19-27.

SANG J, GUO P, XIANGZ, et al . Vehicle detection based on faster-RCNN [J ] . Journal of Chongqing University, 2017, 40(7): 32-36.

ZHANG P, CHEN X J, RUAN Y D, et al . A vehicle classification technique based on sparse coding [J ] . Journal of Xi'an Jiaotong University, 2015, 49(12): 137-143.

贾晓. 套牌车辆的车型识别与检索技术研究[D]. 大连: 大连海事大学, 2016.

TANG Y, ZHANG C Z, GU R S, et al . Vehicle detection and recognition for intelligent traffic surveillance system [J ] . Multimedia Tools Applications, 2017, 76(4): 5817-5832.

LI H, LI Q L, CHENG L L, et al . Convolutional neural network based on multi-color space for vehicle color classification [J ] . Industrial Control Computer, 2019, 32(11): 78-82.

REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE Computer Society, 2016: 779-788.

CHEN Z B, YE D Y, ZHU C X, et al . Object recognition method based on improved YOLOv3 [J ] . Computer Systems and Applications, 2020, 29(1): 49-58.

YAN K, TIAN Y, WANG Y, et al. Exploiting multi-grain ranking constraints for precisely searching visually-similar vehicles[C]//2017 IEEE Conference on Computer Vision. Venice: IEEE, 2017: 562-570.

PASZKE A, GROSS S, MASSA F, et al. PyTorch: an imperative style, high-performance deep learning library[C]//33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver: Curran Associates, 2019: 8024-8035.

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