基于路侧监控的车型分类算法研究

Research on Vehicle Classification Algorithm Based on Road Side Monitoring

交通场景中车辆的检测和分类是发展智能交通的应有之义。本文基于利旧的原则,利用路侧交通监控视频数据,设计改进的YOLOv3深度学习网络模型,使用残差单元以保证卷积神经网络收敛损失,算法采取多尺度特征融合预测的策略,直接在多个尺度的特征图上回归预测车辆边界框和类型,实现车辆的目标快速检测和车型判定。将改进前后的模型在测试集中进行测试,实验结果表明本文提出的深度学习网络在实际交通场景中既满足实时性的要求,又具有良好的车型检测和分类效果。

The detection and classification of vehicles in traffic scenes is an essential part of the development of intelligent transportation. In this paper, based on the principle of benefiting the old, an improved deep learning network is proposed to realize the detection of vehicles and the determination of vehicle models by using the road measurement traffic surveillance video. Experimental results show that the proposed deep learning network has good detection and classification effects in real traffic scenes.