Road Sign Recognition Method Based on Conditional Random Field

Abstract

The invention discloses a road sign recognition method based on a conditional random field, which mainly solves the problem of low accuracy of the existing road sign recognition. The implementation scheme is: 1. Establish a set of landmark color seed points based on pure landmark image data; 2. Calculate the prior color feature map set of images containing landmarks according to the set of landmark color seed points; A collection of color probability distribution maps of road sign images; 4. Use the Markov conditional random field model to fuse the prior color feature maps and color probability distribution maps of road sign images to obtain a fused image; 5. Extract the region of interest in the fused image. 6. Classify and identify the region of interest through a multi-scale convolutional neural network. The invention improves the detection rate of road signs and the recognition accuracy of road signs, and can be used for scene perception in the traffic field.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a road sign recognition method, which can be used for scene perception in the traffic field. Background technique [0002] With the development and progress of social economy, vehicles have been popularized to most families in China. However, while automobiles bring convenience to people's lives, the frequency of traffic accidents is also increasing. Organizations and automobile manufacturers attach great importance to it. One of the effective ways to solve this problem is to accurately and effectively set up road traffic signs to provide drivers with driving information such as prohibition, warning, and instructions, thereby reducing the occurrence of traffic accidents. Therefore, the road sign detection and recognition system has received extensive attention from scholars. In the past ten years, scholars have carried out extensive research in the application fie...

AI Technical Summary

Problems solved by technology

[0005] For the above-mentioned road sign detection methods, due to insufficient consideration of the color and shape characteristics of road signs, the extraction of features is relatively simple, so the efficiency of road sign extraction is low. For the above-mentioned road sign recognition methods, due to the limited ability of traditional feature representation, the accuracy of recognition is relatively low. Low

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