Traffic sign detection and recognition method based on convolutional neural network

Abstract

The invention discloses a traffic sign detection and recognition method based on a convolutional neural network, which belongs to the field of digital image processing and machine learning. The method comprises steps: firstly, an RGB image after pre-processing is converted to HSV color space, and a region of interest is obtained through threshold setting; and then, a two-classification convolutional neural network for distinguishing a traffic sign and a non-traffic sign is designed to judge whether the region of interest is a traffic sign. After the position of a traffic sign is obtained, the traffic sign recognition method based on the convolutional neural network is used, parameters such as the layer number and the characteristic pattern number of the convolutional neural network are adjusted, parameters in the network are learnt through a large amount of training samples, and classes of traffic signs at different positions are further recognized. An experiment shows that the method has good adaptability to deformation, partial occlusion and tilt and the like of the traffic sign, and good performance is presented in aspects of recognition effects and recognition efficiency.

Description

technical field [0001] The invention belongs to the field of digital image processing and machine learning, in particular to a traffic sign recognition method based on a convolutional neural network. Background technique [0002] As early as the early 1980s, some countries began to pay attention to traffic sign recognition, mainly using methods such as template matching, edge detection, and neural networks. The images of traffic sign detection and recognition come from vehicle camera equipment. Although traffic signs generally have obvious color and shape characteristics, due to the complex and changeable outdoor natural conditions, the collected images are easily affected by many unfavorable factors, such as weather Factors such as influence, background interference and object occlusion will directly affect the results of traffic sign detection and recognition. For the detection stage of traffic signs, the main difficulty is that the appearance of traffic signs changes dra...

AI Technical Summary

Problems solved by technology

At the same time, the traditional method for the recognition of traffic signs needs to manually extract features, and then use a large number of extracted features to train the classifier.

Images