A Multi-Signal Light Recognition Method Based on Image Processing

Abstract

The invention discloses a multi-signal light recognition method based on image processing, comprising: (1) performing Top-Hat transformation on a target area; (2) performing grayscale threshold segmentation on the transformed target area to extract a luminous area; (3) Further screen the luminous area through geometric filtering, and mark the candidate area; (4) Train a classifier and use the classifier to classify the candidate area to determine the signal light area; (5) Determine the shape of the signal light through probability analysis. The present invention reduces the influence of halo by preprocessing the training samples, and better restores the shape characteristics of the signal lights. During the processing, the classification obtained by simulating the possible shake and other influences that may occur during the image taking process is increased. device; the present invention is entirely based on image processing, does not need to modify the existing signal light equipment, and its real-time performance is much higher than calling the algorithms for recognizing circular signal lights and recognizing arrow-shaped signal lights respectively.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to an image processing-based multi-signal light recognition method. Background technique [0002] Now more and more research and development departments or scientific research units are concerned about the realization of the intelligent auxiliary system of automobiles, and various methods have been proposed in terms of signal light recognition: [0003] The backplane detection based on NCC template matching proposed by Jin Tao et al. (Traffic light recognition method based on cascaded filtering [M], Journal of Shanghai Jiaotong University (Natural Edition). 2012:1355-60.), but only used in the matching process A single template, the adaptability of the template remains to be explored. [0004] The self-learning template matching algorithm proposed by Raoul de Charette et al. is used for signal light recognition (Real TimeVisual Trafric Lights Recognition Based...

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Problems solved by technology

[0007] Based on the above, it can be seen that although the existing signal light recognition methods can basically meet the requirements of real-time and accurate (within 100ms running time, image recognition rate of more than 90%) to identify signal lights, they are only aimed at circular signal lights or arrow-shaped signal lights. One has been identified, and no corresponding solution has been proposed for the situation where circular signal lights and arrow-shaped signal lights appear in the same scene at the same time in some cities

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