Image Parameters Evaluation for Road Lighting Based on Clustering Analysis

  • Yi Xiong
  • Ning Lv
  • Xufen XieEmail author
  • Yingying Shang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Road lighting is a main factor which impacts on traffic accident rate. The valuable lighting evaluations are the fundament of road lighting design. We propose five classes parameters which come from road lighting images to evaluate the quality of road lighting in this paper. We first calculate 10 image parameters from road lighting images. It includes mean value of gray level, variance of gray level, radiation precision steepness, gray level entropy, second moment of angle, contrast, autocorrelation, inverse difference moment, detail energy, and edge energy. Then, we divide the above 10 parameters into five categories using cluster analysis. These categories are mean value class, variance class, contrast class, detail energy class, and information-related class. Finally, combined with the physical meaning of the parameters, the evaluation index of the traditional road lighting and the characteristics of the human eye, we connect these five categories with the average brightness of pavement, the uniformity of road surface brightness, glare, road sign inducibility, and psychological factors. The experimental results show that the road lighting image parameters have good clustering properties, and the clustered image parameters can reflect the quality of road lighting.


Road lighting evaluation Image characteristic parameters Systematic cluster analysis 


  1. 1.
    Yingkui H, Zhonglin C, Yingpiao L. Light effects of common road light sources under intermediate vision conditions. J Chongqing Univ. 2007;30(1):139–41.Google Scholar
  2. 2.
    Kang W. Research on road lighting detection based on luminance imaging technology. Doctoral dissertation. Zhejiang University; 2016.Google Scholar
  3. 3.
    Yiying W, Doudou C, Liang Z, Jun M, Wenhui N. Image quality evaluation method based on spatial similarity of masking effect. J Hefei Univ Technol: Nat Sci Edn. 2015;10:1339–41.Google Scholar
  4. 4.
    Xiaobing X, Lei C, Jianping W. Research and application of road lighting characteristics based on intermediate vision. J Hefei Univ Technol (Nat Sci). 2013;36(6):704–8.Google Scholar
  5. 5.
    Liyan G, Xianjun M, Naiqiao L, Jinfeng B. Evaluation of apple processing quality based on principal component and cluster analysis. J Agric Eng. 2014;30(13):276–85.Google Scholar
  6. 6.
    Chunhua P, Tonglin Z, Hao L. HVS evaluation method for image quality. Comput Eng Appl. 2010;46(4):149–51.Google Scholar
  7. 7.
    Chen X, Zheng X, Wu C. Portable instrument to measure the average luminance coefficient of a road surface. Meas Sci Technol. 2014;25(3):35203–9.CrossRefGoogle Scholar
  8. 8.
    Cattini S, Rovati L. Low-cost imaging photometer and calibration method for road tunnel lighting. IEEE Trans Instrum Meas. 2012;61(5):1181–92.CrossRefGoogle Scholar
  9. 9.
    China Academy of Building Research. Urban road lighting design standards CJJ45-2015. China Building Industry Press; 2016.Google Scholar
  10. 10.
    Shuqin L, Lifang Y, Gong Y, Xingsheng L. Review of image quality assessment. Chin Sci Technol Pap. 2011;06(7):501–6.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Research Institute of Photonics, Dalian Polytechnic UniversityDalianChina

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