Author:
Wang Xingyu,Feng Haixia,Wang Na,Zhu Maoxin,Ning Erwei,Li Jian
Abstract
AbstractThe real-time and accurate monitoring of severe weather is the key to reducing traffic accidents on highways. Currently, rainy day monitoring based on video images focuses on removing the impact of rain. This article aims to build a monitoring model for rainy days and rainfall intensity to achieve precise monitoring of rainy days on highways. This paper introduces an algorithm that combines the frequency domain and spatial domain, thresholding, and morphology. It incorporates high-pass filtering, full-domain value segmentation, the OTSU method (the maximum inter-class difference method), mask processing, and morphological opening for denoising. The algorithm is designed to build the rain coefficient model Prain coefficient and determine the intensity of rainfall based on the value of Prain coefficient. To validate the model, data from sunny, cloudy, and rainy days in different sections and time periods of the Jinan Bypass G2001 line were used. The aim is to raise awareness about driving safety on highways. The main findings are: the rain coefficient model Prain coefficient can accurately identify cloudy and rainy days and assess the intensity of rainfall. This method is not only suitable for highways but also for ordinary road sections. The model's accuracy has been verified, and the algorithm in this study has the highest accuracy. This research is crucial for road traffic safety, particularly during bad weather such as rain.
Funder
Natural Foundation of Shandong Province
Project of Jinan Municipal Bureau of science and Technology
The research is partially supported by Jinan City’s Self-Developed Innovative Team Project for Higher Educational Institutions
Publisher
Springer Science and Business Media LLC
Reference24 articles.
1. Gunawan, A. A. S. et al. Inferring the level of visibility from hazy image. J. Int. J. Bus. Intell. Data Min. 16(2), 177–189 (2020).
2. Tang, W. et al. A Method for measuring visibility under foggy weather for expressways based on Siamese network. J. Traffic Inf. Secur. 41(4), 122–131 (2023).
3. Ismail, M. K. & Al-Ameen, Z. Adapted single scale retinex algorithm for nighttime image enhancement. J. AL-Rafidain J. Comput. Sci. Math. 16(1), 59–69 (2022).
4. Zhou, I. C. et al. Multi-scale retinex-based adaptive gray-scale transformation method for under water image enhancement. J. Multimed. Tools Appl. 81(2), 1811–1831 (2022).
5. Elhashemi, A. et al. Real-time snow detection based on machine vision and vehicle kinematics: A nonparametric data fusion analysis protocol. J. Saf. Res. 83, 163–180 (2022).