自然交通场景中的车辆颜色识别

doi:10.11896/jsjkx.200800078

摘要/Abstract

摘要： 车辆颜色是重要的车辆信息之一,对其进行识别可为智能交通系统中的车辆识别环节提供更为精准丰富的信息。自然场景中光线的变化会对车辆颜色造成影响,致使难以根据RGB图像直接获得车辆的颜色类别。传统的机器学习方法用于车辆颜色识别时,通常依据经验筛选用于分类的图像特征,易导致分类效果有限等问题,且这些方法一般计算量较大,难以获得实时结果。针对自然场景中车辆颜色信息难以获取和描述这一问题,提出了一种基于多色彩空间信息的深度神经网络模型(MultiColor-Net),使用多个不同尺寸滤波器分别对输入图像在RGB颜色空间和HSV颜色空间上进行特征提取,再将上述不同颜色空间中获得的特征组合,通过全连接网络,获得自然交通场景中目标车辆的颜色分类结果。在真实的智能交通数据集上对比ResNet,Inception v3等深度神经网络模型和本文所提出的MultiColor-Net,结果表明,MultiColor-Net相比于单独识别HSV图像,准确率提高了2.45%左右;相比于单独识别RGB图像,准确率提高了0.8%左右。因此,MultiColor-Net模型能够在真实的交通图像数据上取得较高的识别准确率,同时保持较低的计算复杂度。

关键词: HSV色彩空间, 车辆颜色, 深度神经网络, 颜色识别, 智能交通

Abstract: Vehicle color is one of the significant vehicle details,and the recognition for it can provide more precise and rich information for vehicle identification in the Intelligent Transportation System.In the natural traffic scenes,the vehicle images obtained by cameras are greatly affected by the illumination changes.Therefore,the vehicle color can't be determined directly by the RGB values of the image.The traditional machine learning methods for vehicle color recognition require feature selection steps depending on experience,which may lead to limited classification effect.When applied to actual applications,these approaches probably have high computation cost and are difficult to obtain real-time results.Aiming at the problem that vehicle color information is difficult to gain and describe in natural scenes,a novel deep neural network model based on multi-color spaces is proposed to identify vehicle color in natural traffic scenes(MultiColor-Net).In the MultiColor-Net model,several filters of different sizes are used to extract the features of the input images both in RGB color space and in HSV color space,respectively.Then,the above features obtained in two different color spaces are combined together to get the classification results of vehicle color through a fully connected network.By comparing the experimental results of ResNet,Inception v3 and other deep neural network models with the MultiColor-Net proposed on real intelligent transportation data sets,the accuracy rate of MultiColor-Net is improved by about 2.45% with the HSV images alone,and the accuracy rate is improved by about 0.8% with the RGB images.Consequently,the proposed model,MultiColor-Net,can achieve a high recognition accuracy rate with the real traffic image data,and maintain lower computational complexity.

Key words: Color recognition, Deep neural networks, HSV color space, Intelligent transportation, Vehicle color

中图分类号:

TP183

周欣, 刘硕迪, 潘薇, 陈媛媛. 自然交通场景中的车辆颜色识别[J]. 计算机科学, 2021, 48(6A): 15-20. https://doi.org/10.11896/jsjkx.200800078

ZHOU Xin, LIU Shuo-di, PAN Wei, CHEN Yuan-yuan. Vehicle Color Recognition in Natural Traffic Scene[J]. Computer Science, 2021, 48(6A): 15-20. https://doi.org/10.11896/jsjkx.200800078

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