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http://dx.doi.org/10.6109/jkiice.2020.24.4.467

Design and Implementation of A Smart Crosswalk System based on Vehicle Detection and Speed Estimation using Deep Learning on Edge Devices  

Jang, Sun-Hye (Department of Computer Engineering, Kumoh National Institute of Technology)
Cho, Hee-Eun (Department of Computer Engineering, Kumoh National Institute of Technology)
Jeong, Jin-Woo (Department of Computer Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.4, 2020 , pp. 467-473 More about this Journal
Abstract
Recently, the number of traffic accidents has also increased with the increase in the penetration rate of cars in Korea. In particular, not only inter-vehicle accidents but also human accidents near crosswalks are increasing, so that more attention to traffic safety around crosswalks are required. In this paper, we propose a system for predicting the safety level around the crosswalk by recognizing an approaching vehicle and estimating the speed of the vehicle using NVIDIA Jetson Nano-class edge devices. To this end, various machine learning models are trained with the information obtained from deep learning-based vehicle detection to predict the degree of risk according to the speed of an approaching vehicle. Finally, based on experiments using actual driving images and web simulation, the performance and the feasibility of the proposed system are validated.
Keywords
Deep learning; vehicle detection; speed estimation; crosswalk;
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Times Cited By KSCI : 4  (Citation Analysis)
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