Traffic Sign Segmentation and Recognition Based on Binocular Stereo Vision and Lightweight Neural Network
In order to reduce the calculation of traffic sign image segmentation,this study proposes a traffic sign segmentation and recognition method based on binocular stereo vision and lightweight neural network.A calibrated binocular stereo vision camera is used to collect traffic sign images,which are used as inputs to a lightweight convolutional neural network.The fea-tures of traffic signs are extracted through convolution and pooling operations.In the fully connected layer,extreme learning machine and weight correction methods are used to correct the output weights,thereby obtaining the segmentation results of traffic signs.The experimental results show that this method can effectively collect high-precision traffic sign images and reduce the complexity of image segmentation operation,thereby improving the applicability of traffic sign images.