BP神经网络下的限速交通标志实时检测识别

目前已有很多基于交通标志图片进行道路交通标志识别的系统研究，但存在误识别率较高的问题．因此文中基于视频数据对限速交通标志进行检测与识别，实现了限速交通标志的自动检测定位，并采用反向传播神经网络来进行道路标志的识别; 同时，还采用连续自适应的均值漂移算法和光流法进行视频加速．实验表明，新提出的算法在耗时上缩短50%以上，检测识别准确度在90%以上，适用于相关智能识别领域．

Real-time detection and identification of speed limit traffic signs under the BP neural network

There are a lot of traffic signs based on the picture system of traffic sign recognition, and based on the video data to detect and identify the speed of traffic signs, However, there is a higher error rate. So this paper realizes the automatic detection and localization of speed limit traffic signs, and uses the BP neural network to identify the road signs. Meanwhile, the CamShift method and the optical flow method are used to speed up video, Experiment shows that the algorithm proposed in this paper can shorten the time by more than 50%, and the detection and recognition accuracy is over 90%, which is suitable for the related field of intelligent recognition.