Image Parameters Evaluation for Road Lighting Based on Clustering Analysis

Abstract

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.