基于时序空间关系模型的交通标志识别

Temporal-Spatial model based traffic sign recognition

道路交通标志识别是汽车无人驾驶技术的重要组成部分。通过调整卷积层和池化层数据输入方式,改进具有融合特征的多尺度卷积神经网络,提高识别准确率。依据视频图像的空间连续关系构建时序空间关系模型(Temporal-SpatialModel,TSM),结合多尺度卷积神经网络减少识别数据量,提高处理的效率,实现视频图像交通标志的高效率和高准确率识别。实验结果表明,所提出的算法识别率保持90.36%,在原始图像上运行平均帧率为32fps,有效地改进了基于单帧的交通标志图像识别效率低的问题。

Traffic sign recognition is an important part of driverless car technology.In this paper,by adjusting the data input methods of convolutional layer and pooling layer,the multi-scale convolutional neural network with fusion features is improved to make better recognition accuracy,and the Temporal-Spatial model is constructed according to the spatial relationship of video images.Combining with multi-scale convolutional neural network to reduce the amount of identification data,improve the efficiency of processing,the model achieves the recognition of traffic sign with high efficiency and high accuracy.The experimental results show that the proposed algorithm's recognition rate is 96.81%,and the average frame rate is 32fps on the original image,which effectively improves the low-efficiency of single-frame traffic sign recognition.