The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Parking Lot Vehicle Counting Using a Deep Convolutional Neural Network

Deep Convolutional Neural Network를 이용한 주차장 차량 계수 시스템

  • Received : 2018.09.18
  • Accepted : 2018.10.16
  • Published : 2018.10.31
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Abstract

This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity's self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian.

본 논문에서는 주차장 관리 시스템의 한 부분으로 차량 계수를 위한 감시 카메라 시스템의 컴퓨터 비전과 심층 학습 기반 기법을 제안하고자 한다. You Only Look Once 버전 2 (YOLOv2) 탐지기를 적용하고 YOLOv2 기반의 심층 컨볼루션 신경망(CNN)을 다른 아키텍처와 두 가지 모델로 구성하였다. 제안 된 아키텍처의 효과를 Udacity의 자체 운전 차량 데이터 세트를 사용하여 설명하였다. 학습 및 테스트 결과, 자동차, 트럭 및 보행자 탐지 시 원래 구조(YOLOv2)의 경우 47.89%의 mAP를 나타내는 것에 비하여, 제안하는 모델의 경우 64.30 %의 mAP를 달성하여 탐지 정확도가 향상되었음을 증명하였다.

Keywords

References

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