Earticle

초록

영어

The traditional dual differential luminance algorithm was influenced by the surrounding environment, and was not applicable to accurate detection at night. In view of the interference factors of reality, this paper improved the dual differential luminance algorithm by using the method of amending coefficient. Meanwhile, combined with the traffic emergency control measures under different visibility, ten detection modes were subdivided. The next, an experimental system was built in the Meteorological Observatory of Beijing, and the detection software was developed. At last, the experimental data was compared with the correlation values of scatterometer. The results show that the detection accuracies are raised, and the optimized algorithm and system can be used for highway network visibility detection.

목차

Abstract
 1. Introduction
 2. Detection Principle of Dual Differential Luminance Algorithm
  2.1. Application fundamentals of dual differential luminance algorithm
  2.2. The Original Dual Differential Luminance Algorithm
  2.3. Optimization of Dual Differential Luminance Algorithm
  2.4. Calculating the Correction Parameters with Least Square Method
 3. Realization on Visibility Detection
  3.1. Setting up of Experimental System and Data Collection
  3.2. Research on Transitional Condition
  3.3. Implementation Process of Video Visibility and Software Design
  3.4. Analysis of Experimental Results
 4. Conclusion
 References

키워드

저자

• Zhao Jiandong [ School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China ] Corresponding Author
• Han Mingmin [ School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China ]
• Xin Xin [ Key Laboratory of Road Safety Technologies, Ministry of Transport, Beijing, 100088, China ]
• Li Changcheng [ Key Laboratory of Road Safety Technologies, Ministry of Transport, Beijing, 100088, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605