Multi-characteristic synergic traffic sign detection and identification method

Abstract

The invention discloses a multi-characteristic synergic traffic sign detection and identification method which is performed according to the following steps. A color probability model is established for traffic signs with different colors through the images of traffic sign samples and representative colors are determined out of the traffic signs with different colors so as to obtain probability check lists for the representative colors, train and obtain shape classifying devices for traffic signs belonging to different categories and identifying models. For traffic images to be detected, each probability check list for the representative colors is used first to get the probability images of the traffic images, which are then converted to grey scale maps. An MSER algorithm is used to detect the areas in the grey scale maps which change stably and the areas are regarded as potential windows to be picked up that meet the pre-set height-width ratio. The shape classifying devices then determines whether the potential windows to be picked up contain traffic signs or not, and if they do, the identifying models will identify these corresponding shapes. The method can achieve a better detection and identification effect by combining the characteristics of colors and shapes of traffic signs.

Description

technical field [0001] The invention belongs to the technical field of traffic sign detection and recognition, and more specifically relates to a multi-feature collaborative traffic sign detection and recognition method. Background technique [0002] With the development of economy and technology, intelligent transportation technology has been vigorously developed. As an important part of intelligent transportation technology, the detection and recognition of traffic signs has been paid more and more attention. The detection and recognition of traffic signs is generally based on the captured road images. First, the road images are preprocessed, and then traffic signs are detected from the road images, and finally classified and recognized. [0003] The task of traffic sign detection is to detect the position of traffic signs in the input image, which should have the characteristics of low missed detection rate and low false detection rate. Among them, low missed detection r...

AI Technical Summary

Problems solved by technology

However, in the process of traffic sign recognition of ordinary linear CNN network, since the input of the latter layer is only related to the output of the previous layer, the recognition ability of those too small and blurred images is still limited.

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