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Deep Learning Technologies to Mitigate Deer-Vehicle Collisions

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Deep Learning and Big Data for Intelligent Transportation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 945))

Abstract

Deer-Vehicle Collisions (DVCs) are a growing problem across the world. DVCs result in severe injuries to humans and result in loss of human lives, properties, and deer lives. Several strategies have been employed to mitigate DVCs and include fences, underpasses and overpasses, animal detection systems (ADS), vegetation management, population reduction, and warning signs. The main aim of this chapter is to mitigate deer-vehicle collisions. It proposes an intelligent deer detection system using computer vision and deep learning techniques. It warns the driver to avoid collision with deer. The generated deer detection model achieves 99.3% mean average precision (mAP@0.5) and 78.4% mAP@0.95 at 30 frames per second on the test dataset.

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Authors and Affiliations

  1. Southern Illinois University, Carbondale, IL, USA

    Md. Jawad Siddique & Khaled R. Ahmed

Authors
  1. Md. Jawad Siddique
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  2. Khaled R. Ahmed
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Correspondence to Khaled R. Ahmed .

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Editors and Affiliations

  1. School of Computing, Southern Illinois University, Carbondale, IL, USA

    Khaled R. Ahmed

  2. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Jawad Siddique, M., Ahmed, K.R. (2021). Deep Learning Technologies to Mitigate Deer-Vehicle Collisions. In: Ahmed, K.R., Hassanien, A.E. (eds) Deep Learning and Big Data for Intelligent Transportation. Studies in Computational Intelligence, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-65661-4_5

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