Journal of information and communication convergence engineering

  • 제18권1호
  • /
  • Pages.28-32
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  • 2020
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Comparison of Deep-Learning Algorithms for the Detection of Railroad Pedestrians

  • Fang, Ziyu (Department of Computer Software Engineering, Silla University) ;
  • Kim, Pyeoungkee (Department of Computer Software Engineering, Silla University)
  • 투고 : 2019.08.15
  • 심사 : 2020.03.16
  • 발행 : 2020.03.31
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초록

Railway transportation is the main land-based transportation in most countries. Accordingly, railway-transportation safety has always been a key issue for many researchers. Railway pedestrian accidents are the main reasons of railway-transportation casualties. In this study, we conduct experiments to determine which of the latest convolutional neural network models and algorithms are appropriate to build pedestrian railroad accident prevention systems. When a drone cruises over a pre-specified path and altitude, the real-time status around the rail is recorded, following which the image information is transmitted back to the server in time. Subsequently, the images are analyzed to determine whether pedestrians are present around the railroads, and a speed-deceleration order is immediately sent to the train driver, resulting in a reduction of the instances of pedestrian railroad accidents. This is the first part of an envisioned drone-based intelligent security system. This system can effectively address the problem of insufficient manual police force.

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참고문헌

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피인용 문헌

  1. 제조업 근로자 안전관리를 위한 데이터셋 구축과 모델 학습 vol.25, pp.7, 2021, https://doi.org/10.6109/jkiice.2021.25.7.890