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교통 영상 빅데이터 처리를 위한 Yolo 기반 광원 객체 탐지

Yolo based Light Source Object Detection for Traffic Image Big Data Processing

  • 강지수 (경기대학교 컴퓨터과학과) ;
  • 심세은 (경기대학교 컴퓨터공학부) ;
  • 조선문 (배재대학교 IT교육학과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • Kang, Ji-Soo (Department of Computer Science, Kyonggi University) ;
  • Shim, Se-Eun (Division of Computer Science and Engineering, Kyonggi University) ;
  • Jo, Sun-Moon (Department of Computer Information Technology Education, Paichai University) ;
  • Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
  • 투고 : 2020.07.02
  • 심사 : 2020.08.20
  • 발행 : 2020.08.28

초록

교통안전에 대한 관심이 높아짐에 따라 교통사고의 발생률을 줄이는 자율 주행에 대한 연구가 지속적으로 진행되고 있다. 객체의 인식과 탐지는 자율 주행을 위한 필수적인 요소이다. 때문에 도로 상황을 판단하기 위하여 교통 영상 빅데이터에서 객체 인식 및 탐지에 대한 연구가 활발히 진행 중이다. 하지만 기존 연구들은 대부분 주간 데이터만 사용하기 때문에 야간 도로에서 객체 인식이 어렵다. 특히 광원 객체의 경우 빛 번짐과 백화 현상으로 인해 주간의 특징을 그대로 사용하기 어렵다. 따라서 본 연구에서는 교통 영상 빅데이터 처리를 위한 Yolo 기반 광원 객체 탐지를 제안한다. 제안하는 방법은 야간 교통 영상을 대상으로 색상 모델 변화를 적용하여 이미지 처리를 수행한다. 이미지 처리를 통해서 객체의 특징을 추출하여 객체의 후보군을 결정한다. 후보군 데이터를 활용하여 딥러닝 모델을 통해 야간 도로에서 광원 객체 탐지의 인식률을 높이는 것이 가능하다.

As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.

키워드

참고문헌

  1. TAAS. (2020). Traffic Accident Analysis System. http://taas.koroad.or.kr
  2. KOSIS. (2020). KOrean Statisitcal Information Service. http://kosis.kr
  3. D. J. Fagnant & K. Kockelman. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181. DOI: 10.1016/j.tra.2015.04.003
  4. Korea Ministry of Government Legislation. (2020). Automobile Management Law. http://www.law.go.kr
  5. E. Frazzoli, M. A. Dahleh & E. Feron. (2002). Real-time motion planning for agile autonomous vehicles. Journal of guidance, control, and dynamics, 25(1), 116-129. DOI: 10.2514/2.4856
  6. H. Nicolas & J. M. Pinel. (2006). Joint moving cast shadows segmentation and light source detection in video sequences. Signal processing: Image communication, 21(1), 22-43. DOI: 10.1016/j.image.2005.06.001
  7. L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu & M. Pietikainen. (2020). Deep learning for generic object detection: A survey. International journal of computer vision, 128(2), 261-318. DOI: 10.1007/s11263-019-01247-4
  8. J. Redmon, S. Divvala, R. Girshick & A. Farhadi. (2016). You only look once: Unified, real-time object detection. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788. DOI: 10.1109/CVPR.2016.91
  9. F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu & T. Darrell. (2020). BDD100K: A diverse driving dataset for heterogeneous multitask learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2636-2645. arXiv: 1805.04687
  10. Y. Zhang, J. Xue, G. Zhang, Y. Zhang & N. Zheng. (2014). A multi-feature fusion based traffic light recognition algorithm for intelligent vehicles. In Proceedings of the 33rd Chinese Control Conference, IEEE, 4924-4929. DOI: 10.1109/ChiCC.2014.6895775
  11. K. Behrendt, L. Novak & R. Botros. (2017). A deep learning approach to traffic lights: Detection, tracking, and classification, IEEE International Conference on Robotics and Automation (ICRA), 1370-1377. DOI: 10.1109/ICRA.2017.7989163
  12. S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat & J. Jatakia. (2017). Human skin detection using RGB, HSV and YCbCr color models. arXiv, arXiv:1708.02694.
  13. D. Shin, R. C. Park & K. Chung. (2020). Decision Boundary-based Anomaly Detection Model using Improved AnoGAN from ECG Data. IEEE Access, 8(1), 108664-108674. DOI: 10.1109/ACCESS.2020.3000638
  14. A. Bochkovskiy, C. Y. Wang & H. Y. M. Liao. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv:2004.10934.
  15. J. Baek & K. Chung. (2020). Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression. IEEE Access, 8, 18171-18181 DOI: 10.1109/ACCESS.2020.2968393.
  16. J. Kang, J. Baek & K. Chung. (2020). PrefixSpan based Pattern Mining using Time Sliding Weight for Streaming Data. IEEE Access, 8(1), 124833-124844. DOI: 10.1109/ACCESS.2020.3007485