基于优化YOLO-V4的交通标志检测识别方法

Traffic Sign Detection and Recognition Method Based on Optimized YOLO-V4

交通标志检测识别是自动驾驶系统的核心功能,为了实时准确地识别交通标志,在YOLO-V4的基础上进行改进,并结合了空间金字塔池化(Spatial Pyramid Pooling,SPP)模块。首先,为了提高分辨率和增大感受野,将原特征图3个尺度的分辨率更改为26×26和52×52;然后,在连接层中添加SPP模块,消除网络对固定尺度的约束,在最大池化层中得到最优特征,改善网络性能。实验中,利用行车记录仪采集各种交通标志图像,与其他优秀方法相比,所提方法取得了更优的性能,其平均检测识别准确度达99.0%,平均检测时间为0.449 s,达到了实时检测的要求。

Traffic sign detection and recognition is the core function of automatic driving system.In order to identify traffic signs in real time and accurately,a method is improved on the basis of YOLO-V4 and combined with the spatial pyramid pool(SPP)module.Firstly,to increase the resolution and receptive field,the resolution of the three scales of the original feature map is changed to 26×26 and 52×52.Then,SPP module is added to the connection layer to eliminate the constraints of the network on the fixed scale,obtain the optimal characteristics in the maximum pooling layer and improve the network performance.Experiment uses the tachograph to collect various traffic sign images,compared with other excellent methods,the proposed method achieves better performance.The average detection and recognition accuracy of the proposed method is 99.0%,and the average detection time is 0.449 s,which meets the requirements of real-time detection.