Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Design and Implementation of A Smart Crosswalk System based on Vehicle Detection and Speed Estimation using Deep Learning on Edge Devices

엣지 디바이스에서의 딥러닝 기반 차량 인식 및 속도 추정을 통한 스마트 횡단보도 시스템의 설계 및 구현

  • 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)
  • Received : 2020.02.10
  • Accepted : 2020.02.28
  • Published : 2020.04.30
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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.

최근 우리나라의 자동차 보급률이 증가함에 따라 교통사고 발생 건수 또한 증가하고 있다. 특히, 차량간 사고뿐만 아니라 횡단보도 근처에서의 인명 사고 또한 증가하고 있어 횡단보도 교통안전에 대한 주의가 더욱 요구되고 있다. 본 논문에서는 NVIDIA Jetson Nano급의 엣지 디바이스를 이용하여 횡단보도에 접근하는 차량을 인식하고 속도를 추정함으로써 횡단보도 주위 안전 상태를 예측하는 시스템을 제안한다. 딥러닝 기반 차량 위치 인식을 통하여 얻은 정보들을 바탕으로 다양한 기계 학습 기법을 학습시켜 차량 속도에 따른 위험 정도를 예측한다. 마지막으로, 실제 주행 영상을 이용한 실험 및 웹 시뮬레이션을 통해 제안하는 시스템의 성능과 활용 가능성을 검증하였다.

Keywords

References

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