Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (6): 194-200.DOI: 10.3778/j.issn.1002-8331.1812-0166

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Research on Taillight Detection Algorithm for Multi-Scale Cascade R-FCN

BAI Bo, XIE Gang, XU Xinying   

  1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
  • Online:2020-03-15 Published:2020-03-13



  1. 太原理工大学 电气与动力工程学院,太原 030024


The taillight detection of the front vehicle is a research hotspot of environmental awareness in automatic driving. In order to detect the taillights of the vehicle in real time in a complex urban environment, Region-based Fully Convolutional Networks(R-FCN) is proposed into the taillight detection. A vehicle taillight detection algorithm based on multi-scale cascade R-FCN is proposed. Through the cross-layer connection in the network, the underlying features and high-level semantics of the taillights are merged, and the batch normalization layer is added to accelerate the convergence speed of the network, and an improved R-FCN sub-network is obtained. Then with a series of input data with different intersection-over-union, the trained R-FCN sub-networks are cascaded to obtain the final detection network. At the same time, the improved non-maximum suppression is utilized in inference stage to obtain the most accurate detection results. The test results show that the method achieves an overall mean average precision of 94.04% on the CVPR dataset, and the average detection time of a single image takes 31 ms, which has better performance in detection speed and accuracy.

Key words: vehicle taillight detection, Region-based Fully Convolutional Networks(R-FCN), cascaded network, multi-scale feature fusion, batch normalization, non-maximum suppression


前方车辆尾灯检测是自动驾驶中环境感知的研究热点,为在复杂城市环境下实时检测车辆尾灯,将基于区域的全卷积网络(Region-based Fully Convolutional Networks,R-FCN)引入尾灯检测,提出了一种基于多尺度级联R-FCN的车辆尾灯检测算法。通过网络中的跨层连接融合尾灯的底层特征和高层语义,并加入批次归一化层加快网络的收敛速度,得到改进的R-FCN子网络,将一系列在不同交并比输入数据上训练的R-FCN子网络级联得到最终的检测网络。同时预测阶段采用改进的非极大值抑制获得最精准的检测结果。检测结果表明,该方法在CVPR数据集上获得总体94.04%的平均精度,单张图片平均检测耗时31 ms,在检测速度和精度上均有较好的性能。

关键词: 车辆尾灯检测, 基于区域的全卷积网络(R-FCN), 级联网络, 多尺度特征融合, 批次归一化, 非极大值抑制