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Accelerating Classification of Symbolic Road Markings (SRMs) in Autonomous Cars Through Computer Vision-Based Machine Learning

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Internet of Unmanned Things (IoUT) and Mission-based Networking

Part of the book series: Internet of Things ((ITTCC))

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Abstract

Road markings are an essential and integral part of safe driving where main landmarks are used to guide drivers. Develo** a robust road-marking interpretation system is challenging because of several aspects such as changes in light conditions, varying weather conditions, shadows, and faded signs and text. This chapter investigates the use of deep learning methods such as convolutional neural networks (CNNs) to classify symbolic road markings. Previous work in the literature has reported techniques which are predominantly based on feature extraction and template matching which restricts the use of such methods in real time. For autonomous vehicles, road markings need to be interpreted in real time to make timely decisions. This book chapter investigates and presents CNN-based image preprocessing methods to detect road markings for autonomous vehicles. Several CNN architectures were investigated with multiple convolutional, max pooling, and fully connected layers. This chapter will contribute by develo** a model with low computational requirements which is essential for autonomous vehicles. It will further explore state-of-the-art image preprocessing methods such as grayscaling, top-hat, and Otsu’s method. The performance of the proposed road-marking detector will be benchmarked using a public dataset with labeled road-marking images.

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Acknowledgments

The author would like to thank the M.Sc. thesis student(s) for contributing to the experimental simulations conducted in the IoT lab within the School of Computing, Electronics and Maths, Coventry University, UK.

Disclaimer

This is an original work conducted and supervised by Dr. Arfan Ghani at Coventry University, UK. This book chapter contains at least 20–30% unpublished work. Readers and researchers of this chapter are referred to the author’s previously published work [21, 22], where some of the methods and techniques are further elaborated.

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Authors and Affiliations

  1. Computer Science and Engineering, School of Engineering, American University of Ras al Khaimah, Ras al Khaimah, UAE

    Arfan Ghani

  2. College of Engineering and IT, University of Dubai, Dubai, UAE

    Rahat Iqbal

Authors
  1. Arfan Ghani
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  2. Rahat Iqbal
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Correspondence to Arfan Ghani .

Editor information

Editors and Affiliations

  1. Université Amar Telidji de Laghouat, Laghouat, Algeria

    Chaker Abdelaziz Kerrache

  2. Polytechnic University of Valencia, Valencia, Spain

    Carlos Calafate

  3. UAE University, Al Ain, United Arab Emirates

    Abderrahmane Lakas

  4. University of Hassan II Casablanca, Casablanca, Morocco

    Mohamed Lahby

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Ghani, A., Iqbal, R. (2023). Accelerating Classification of Symbolic Road Markings (SRMs) in Autonomous Cars Through Computer Vision-Based Machine Learning. In: Kerrache, C.A., Calafate, C., Lakas, A., Lahby, M. (eds) Internet of Unmanned Things (IoUT) and Mission-based Networking. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-33494-8_6

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  • DOI: https://doi.org/10.1007/978-3-031-33494-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33493-1

  • Online ISBN: 978-3-031-33494-8

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