【摘要】 深度学习是基于图像的交通标志检测和识别处理的研究热点,已取得了显著的效果。针对基于车载视频的交通标志检测和识别处理问题,文中根据图像序列的帧间时空连续关系构建了时空关系模型(Spatiotemporal Model,STM),并与多尺度卷积神经网络(Convolutional Neural Networks,CNN)相结合,提出了一种基于时空卷积神经网络(Spatiotemporal-CNN,ST-CNN)的交通标志实时检测识别算法。实验结果表明,该算法可对视频图像序列中的同一交通标志实现检测、筛选、追踪和识别处理,在保证高准确率的同时,可有效减少CNN的数据输入,降低系统资源占用量,提高计算效率,满足了视频中交通标志检测识别的实时性要求。算法平均每帧耗时26.82 ms,且识别准确率达到96.94%。更多还原

【Abstract】 At present,deep learning is a research hotspot based on image traffic sign detection and recognition proces-sing,and has achieved remarkable results.Aiming at the problem of traffic sign detection and recognition based on car-video,this paper proposed a real-time detection and recognition algorithm for traffic signs based on Spatiotemporal-CNN(ST-CNN).It constructs a Spatiotemporal model(STM) based on the spatiotemporal relationship between frames of image sequences,and combines the STM with Convolutional Neural Network(CNN).The experimental results show that the algorithm can detect,screen,track and identify the same traffic sign in the video image sequence.It can effectively reduce CNN data input and system resource consumption,and improve computational efficiency,while ensuring high accuracy.It satisfies the real-time requirements of traffic sign detection and recognition in video.The algorithm takes an average of 26.82 milliseconds per frame and the recognition accuracy reaches 96.94%.更多还原