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

Abstract:

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