Research on highway rain monitoring based on rain monitoring coefficient
Xingyu Wang, Haixia Feng, Na Wang, Maoxin Zhu, Erwei Ning, Jian Li
TL;DR
This paper introduces a new method for monitoring rain on highways using a rain coefficient model to improve driving safety during bad weather.
Contribution
The novel contribution is the development of a rain coefficient model (Prain coefficient) for precise rain and rainfall intensity monitoring.
Findings
The rain coefficient model accurately identifies cloudy and rainy days.
The model assesses rainfall intensity and has the highest accuracy compared to other methods.
The algorithm is applicable to both highways and ordinary road sections.
Abstract
The 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…
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Taxonomy
TopicsAir Quality Monitoring and Forecasting · Fire Detection and Safety Systems · Image Enhancement Techniques