inis 22(31): 2

Research Article

Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles

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  • @ARTICLE{10.4108/eetinis.v9i31.985,
        author={Daniel Suarez-Mash and Arfan Ghani and Chan H. See and Simeon Keates and Hongnian Yu},
        title={Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={9},
        number={31},
        publisher={EAI},
        journal_a={INIS},
        year={2022},
        month={5},
        keywords={convolutional neural networks, symbol road marking, autonomous cars, intelligent systems, system design, embedded systems},
        doi={10.4108/eetinis.v9i31.985}
    }
    
  • Daniel Suarez-Mash
    Arfan Ghani
    Chan H. See
    Simeon Keates
    Hongnian Yu
    Year: 2022
    Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles
    INIS
    EAI
    DOI: 10.4108/eetinis.v9i31.985
Daniel Suarez-Mash1, Arfan Ghani2,*, Chan H. See3, Simeon Keates4, Hongnian Yu3
  • 1: Coventry University
  • 2: American University of Ras Al Khaimah
  • 3: Edinburgh Napier University
  • 4: University of Chichester
*Contact email: Ghani_786@yahoo.com

Abstract

To make autonomous cars as safe as feasible for all road users, it is essential to interpret as many sources of trustworthy information as possible. There has been substantial research into interpreting objects such as traffic lights and pedestrian information, however, less attention has been paid to the Symbolic Road Markings (SRMs). SRMs are essential information that needs to be interpreted by autonomous vehicles, hence, this case study presents a comprehensive model primarily focused on classifying painted symbolic road markings by using a region of interest (ROI) detector and a deep convolutional neural network (DCNN). This two-stage model has been trained and tested using an extensive public dataset. The two-stage model investigated in this research includes SRM classification by using Hough lines where features were extracted and the CNN model was trained and tested. An ROI detector is presented that crops and segments the road lane to eliminate nonessential features of the image. The investigated model is robust, achieving up to 92.96 percent accuracy with 26.07 and 40.1 frames per second (FPS) using ROI scaled and raw images, respectively.