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Recognition of Vehicle Warning Indicators

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Trends in Data Engineering Methods for Intelligent Systems (ICAIAME 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 76))

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Abstract

Testing of a vehicle instrument cluster for design validation is important especially for autonomous vehicles. As autonomous vehicles require dedicated test scenarios, logging warning instrument cluster’s indicators/lights and testing their functionality needs to be validated. In addition, testing instrument cluster with different vehicle ECUs, detecting delays and sensitivity are another needs. In this work, we present a camera-based system to detect lights of vehicle instrument cluster. Our goal is to detect lights of instrument cluster through camera-based system in a video-stream. Instead of manual, inspection which is carried out by human, a machine vision-based system is developed for automation of this validation task. We used a cloud system, namely Microsoft Azure Cloud, for object detection. It is used to recognize lights of the cluster. We created our own dataset and it contains 18 kinds of warning indicators and 360 images in which different combinations of indicator lights exist. We tested our model and calculated the precision, recall, and mAPas 97.3%, 82.2%, and 91.4%, respectively. Even under the limited examples of labeled data and unbalanced data set conditions, the results are promising to recognize vehicle warning indicators.

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Author information

Authors and Affiliations

  1. Information Systems Engineering Department, Kocaeli University, 41001, Izmit, Turkey

    Ali Uçar & Süleyman Eken

Authors
  1. Ali Uçar
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  2. Süleyman Eken
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Süleyman Demirel University, Isparta, Turkey

    Tuncay Yigit

  3. Department of Computer Science, Alexandru Ioan Cuza University, Iasi, Romania

    Bogdan Patrut

  4. Department of Computer Science and Engineering, University of Westminster, London, UK

    Anastassia Angelopoulou

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Uçar, A., Eken, S. (2021). Recognition of Vehicle Warning Indicators. In: Hemanth, J., Yigit, T., Patrut, B., Angelopoulou, A. (eds) Trends in Data Engineering Methods for Intelligent Systems. ICAIAME 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-79357-9_47

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