Intelligent recognition of traffic sign images based on visual communication technology

Aiming at the defects of current traffic sign image recognition, such as high error rate and poor recognition in real time, the intelligent recognition method of traffic sign image based on visual communication technology is proposed with the main objective of improving the accuracy of traffic sign image recognition. This work proposes traffic sign recognition using visual communication preprocessing coupled with HOG and SVM-based classification, which attempts to overcome limitations in accuracy and in real time posed by present approaches. Firstly, the traffic sign images are collected and pre-processed according to the visual communication technique to improve the quality of traffic sign images; Then, based on the analysis of the traffic sign recognition system, a scheme for traffic sign detection and recognition is proposed. The system performs normalization operations on the captured images after greyscaling and smoothing, and unifies the image size to facilitate feature extraction. The pipeline filters noise using Gabor filtering, extracts features with HOG, and classifies features robustly with an SVM using an RBF kernel. Feature segmentation of the processed images and feature extraction using the HOG algorithm are performed. Finally, the SVM risk algorithm is used to train the database to achieve efficient, automatic, and fast classification of traffic signs. A dataset of traffic sign image recognition is used to simulate the experiment. The results show that the proposed Method improves the correct rate of intelligent recognition of traffic sign images, the recognition speed is greatly improved. Recognition accuracies of up to 96% were achieved in experiments on benchmark datasets, with inference times significantly lower than those of previous methods. The intelligent recognition results of traffic sign images are considerably better than other current methods, which have higher practical application value.