[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.6109/jicce.2020.18.1.28

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)

Publication Information

Journal of information and communication convergence engineering / v.18, no.1, 2020 , pp. 28-32 More about this Journal

Abstract

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.

Keywords

Deep-learning; MobileNet; Pedestrian railroad accident;

Citations & Related Records

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