Research On Vehicle Visual Road Traffic Sign Detection Method Abstract

As a key technology of intelligent transportation system,traffic sign detection and recognition has a wide range of application scenarios in assisted driving and unmanned driving.However,due to the complex road environment,the traffic sign detection and recognition process will be disturbed by natural conditions and internal hardware and other factors,making it difficult for the detection and recognition algorithm to meet commercial application requirements.Therefore,this paper conducts research on the detection and recognition of road traffic signs in natural scenes,and the main work is as follows:In view of the lack of a unified traffic sign data set in China,this paper collects traffic sign data under real road conditions,and transforms and expands the collected data through pre-processing methods in the early stage.The pre-processing methods include: Gamma correction and histogram equalization Preprocessing technology enhances the internal information of the image and reduces the impact of light;meanwhile,it uses median filtering to eliminate part of the image noise and protect the image edge information;the framework of the traffic sign detection and recognition system is designed at the end of this chapter,which paves the way for future work Experimental basis.First,for the detection and recognition method of road traffic signs,a traffic sign detection method based on color information and fusion feature moment input support vector machine is proposed.The detection method in this paper first compares the color expression capabilities of different color spaces,uses color features for image segmentation,and uses morphological processing to eliminate most of the irrelevant information in the image to obtain recognition candidate regions;Select the serial connection method to obtain new features;transmit the new feature information to the SVM for classification training,and distinguish the color shape of the image road traffic signs.Secondly,for the classification and recognition of traffic signs,a traffic sign recognition method with improved Le Net-5 network model is proposed.The optimization content includes: reducing the original model convolution kernel parameters,using Re LU activation function instead of sigmoid activation function,reducing the parameters of the fully connected layer,reducing the amount of calculation,and finally introducing the idea of inception module to enhance the network's ability to extract features.The results show that the algorithm can effectively improve the recognition accuracy.Finally,this paper applies the algorithm to engineering practice,taking the intelligent car as the experimental platform,the raspberry pie micro computer as the image processing platform,through the network communication and the remote computer together assists the traffic sign detection,the experiment proves that the algorithm can effectively identify the traffic sign and make the simple preset response in the practical application.