METODOLOGIA DE DETECÇÃO E RECONHECIMENTO DE SEMÁFOROS UTILIZANDO REDES NEURAIS ARTIFICIAIS
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Data
2016-03-22
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Universidade Federal do Maranhão
Resumo
Urban roads are very complex. The increase in the flow of vehicles in the cities has
contributed to traffic accidents. Researches for accident reduction show that the traffic lights
are effective in reducing accidents. Traffic lights can minimize the occurrence of accidents at
intersections and crosswalks. The implementation of traffic light signals shows significant
advantages, otherwise reveals some problems such as the failure to detect road signs by
drivers on urban roads. This fact is related to excessive visual information, the stress of the
drivers and/or eyestrain makes the drivers lose their attention. These reasons motivated
researches about intelligent vehicles. This work aims to develop a methodology to detect and
recognize traffic lights, to be applied in smart vehicles. This methodology can contribute to
the Advanced Driver Support Systems (ADAS), which assists drivers, especially those with
partial vision impairment.
Image processing techniques are used to develop the detection methodology. Back project and
global thresholding are combined to find light points. Local thresholding techniques are
applied to calculate the symmetry between the radius and the center of the light points to
segment the traffic light body. The first step got an average rate of 99% of detection. The
features of the traffic lights are extracted using Haralick texture measures, with the inclusion
of color and shape information. The data generated by the feature extraction step were preprocessed using the SMOTE technique to balance the database. The recognition and
identification of the traffic lights state are made by an artificial neural network using
Multilayer-Perceptron (MLP). The backpropagation learning algorithm are used in the
network training. The validation results show an average recognition rate of 98%.
Descrição
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detecção de semáforos; Advanced Driver Support Systems; histograma retroprojetado; visão computacional e redes neurais artificiais, traffic lights detection; Advanced Driver Support Systems; back project; computer vision; artificial neural network
Citação
SOARES, Julio Cesar da Silva. METODOLOGIA DE DETECÇÃO E RECONHECIMENTO DE SEMÁFOROS UTILIZANDO REDES NEURAIS ARTIFICIAIS. 2017. [85 folhas]. Dissertação( PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO/CCET) - Universidade Federal do Maranhão, [São Luis] .