Method for intelligently recognizing state of circular traffic light

Abstract

The invention discloses a method for intelligently recognizing the state of a circular traffic light, and the method comprises the steps: carrying out the preprocessing of a road image, wherein the key is color component filtering; carrying out the color segmentation through a proper threshold value in an HSV color space obtained through dictionary learning, obtaining three binary images, and saving the conventional gray scale image processing operation; designing a dynamic multistage filter based on the features of the circular traffic light; carrying out the dynamic filtering operation, andquickly screening out traffic light candidate connected regions; finally marking a signal light image in an image through a black body growth covering method, analyzing and calibrating a color histogram of the image, calculating a color discrimination coefficient, and obtaining the state of the traffic light through the color discrimination coefficient. The method can quickly achieve the effectivediscrimination of the real-time state of the traffic light, assists an intelligent vehicle to read the current information of the traffic light, can be used for the obtaining of the display state ofthe traffic light in intelligent driving, and is great in application value in the field of intelligent driving.

Description

technical field [0001] The invention relates to an image processing method of a traffic signal lamp, in particular to a method for intelligently identifying the state of a circular traffic signal lamp. Background technique [0002] Traffic signal recognition, as an important part of assisted driving of unmanned vehicles, has received extensive attention. Circular traffic lights are the most common form of signal lights. Real-time and accurate identification of the status of circular traffic lights is conducive to the development of automotive assisted driving systems, and even the development of unmanned driving. Therefore, circular traffic light recognition has important research value and broad application prospects. Its practical significance is mainly reflected in the following three aspects: [0003] 1. Providing real-time intersection traffic information for unmanned vehicles is an indispensable part of the unmanned driving system. [0004] 2. It can be used as an a...

AI Technical Summary

Problems solved by technology

At present, a large number of recognition methods use template matching and support vector machines for classification operations, but both methods have shortcomings.

Template matching recognition is extremely sensitive to the established template, and a slight change in the recognition object may lead to unsuccessful template matching and recognition failure

The quality of the training samples will limit the performance of the support vector machine. In complex recognition scenarios, when the training is insufficient or the sample quality is not good, the support vector machine may lose its recognition ability

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