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Embedded real-time speed limit sign recognition using image processing and machine learning techniques

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Abstract

The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 \(\upmu\)s to recognize a sign, while kNN took 11,721 \(\upmu\)s and SVM 12,595 \(\upmu\)s. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.

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Acknowledgments

Pedro Pedrosa Rebouças Filho acknowledges the sponsorship from the Instituto Federal do Ceará (IFCE) via grants PROINFRA/2013, PROAPP/2014 and PROINFRA/2015. Victor Hugo C. Albuquerque acknowledges the sponsorship from the Brazilian National Council for Research and Development (CNPq) through grants 470501/2013-8 and 301928/2014-2. Authors gratefully acknowledge the funding of Project NORTE-01-0145-FEDER-000022, SciTech—Science and Technology for Competitive and Sustainable Industries, co-financed by Programa “Operacional Regional do Norte (NORTE2020)” through “Fundo Europeu de Desenvolvimento Regional (FEDER).”

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Authors and Affiliations

  1. Laboratório de Processamento Digital de Imagens e Simulação Computacional, Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Ceará, Brazil

    Samuel L. Gomes, Elizângela de S. Rebouças, Edson Cavalcanti Neto & Pedro P. Rebouças Filho

  2. Departamento de Ciência da Computação, Universidade Estadual Paulista, Bauru, São Paulo, Brazil

    João P. Papa

  3. Programa de Pós-Graduação em Informática Aplicada, Laboratório de Bioinformática, Universidade de Fortaleza, Fortaleza, CE, Brazil

    Victor H. C. de Albuquerque

  4. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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  1. Samuel L. Gomes
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Correspondence to Pedro P. Rebouças Filho.

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Gomes, S.L., Rebouças, E.d.S., Neto, E.C. et al. Embedded real-time speed limit sign recognition using image processing and machine learning techniques. Neural Comput & Applic 28 (Suppl 1), 573–584 (2017). https://doi.org/10.1007/s00521-016-2388-3

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