计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 15-20.doi: 10.11896/jsjkx.200800078
周欣, 刘硕迪, 潘薇, 陈媛媛
ZHOU Xin, LIU Shuo-di, PAN Wei, CHEN Yuan-yuan
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。
中图分类号:
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