Research And Implementation Of Road Sign Recognition Algorithm Based On Deep Learning

In recent years,China's national economy has continuously improved.By the end of 2017,China's car ownership has exceeded 200 million,and cars have played an extremely important role in residents' travel.In order to better ensure driving safety and liberate the driver from the long and tiring driving behavior,research on autonomous driving began to appear and developed in full swing.Many internet giants and traditional depots began to invest in the field of autonomous driving.Traffic signs are a common traffic assist facility,which provides drivers with abundant road information,especially character traffic signs.The rich high-level semantic information contained in the traffic signs is used to relieve traffic congestion and improve road traffic safety.Significance.Traffic sign text is a kind of scene text.At present,there is relatively little research on traffic sign detection and recognition at home and abroad,which has not yet formed an open and uniform data set for research,especially Chinese traffic sign text.In this paper,a representative and challenging data set is established through image acquisition and processing.An algorithm based on depth neural network is proposed to detect and recognize the collected images.In this paper,the guiding signs in the character traffic signs are selected as the research objects.A representative and challenging data set is established through image acquisition and processing.A deep neural network based algorithm is proposed to collect the captured images.Detection and identification.At the same time,compared with the current popular algorithms.Word candidate area.The method directly predicts the text bounding box in any direction by proposing a new quadrilateral representation regression model.Through the comprehensive evaluation and comparison of some commonly used benchmarks for text detection,word recognition and end-to-end scene text recognition,the advantages of this paper algorithm are clearly verified.The popular object detection algorithm(SSD)is improved to be suitable for text object detection.A kind of deep neural network(CRNN),which combines CNN and RNN,is used to recognize the text.At the same time,a new idea is proposed in this paper.The text detection algorithm of the optimization algorithm is optimized by the result of text recognition,which makes the whole network end-to-end trainable.In this paper,the relatedAmong all the experimental results,the algorithm proposed in this paper has achieved the best performance in horizontal text data-sets and multi-directional text data-sets,and is highly efficient.This thesis also carried out experiments on the data sets collected by ourselves,the experimental results show that the end-to-end full convolutional network for detecting and identifying text in any direction has high stability and efficiency,which can be generated in a messy background.The results show that the algorithm has achieved good results in Chinese multi-directional data sets.