Research on highway rain monitoring based on rain monitoring coefficient
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53360-1 |
| _version_ | 1827327746761555968 |
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| author | Xingyu Wang Haixia Feng Na Wang Maoxin Zhu Erwei Ning Jian Li |
| author_facet | Xingyu Wang Haixia Feng Na Wang Maoxin Zhu Erwei Ning Jian Li |
| author_sort | Xingyu Wang |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-03-07T15:06:47Z |
| format | Article |
| id | doaj.art-ff0c0e7219a6417682b504fc5e0daf71 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:06:47Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ff0c0e7219a6417682b504fc5e0daf712024-03-05T18:53:58ZengNature PortfolioScientific Reports2045-23222024-02-0114111210.1038/s41598-024-53360-1Research on highway rain monitoring based on rain monitoring coefficientXingyu Wang0Haixia Feng1Na Wang2Maoxin Zhu3Erwei Ning4Jian Li5School of Transportation and Logistics Engineering, Shandong Jiaotong UniversitySchool of Transportation and Logistics Engineering, Shandong Jiaotong UniversitySchool of Transportation and Logistics Engineering, Shandong Jiaotong UniversitySchool of Transportation and Logistics Engineering, Shandong Jiaotong UniversitySchool of Transportation and Logistics Engineering, Shandong Jiaotong UniversitySchool of Transportation and Logistics Engineering, Shandong Jiaotong UniversityAbstract 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 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.https://doi.org/10.1038/s41598-024-53360-1 |
| spellingShingle | Xingyu Wang Haixia Feng Na Wang Maoxin Zhu Erwei Ning Jian Li Research on highway rain monitoring based on rain monitoring coefficient Scientific Reports |
| title | Research on highway rain monitoring based on rain monitoring coefficient |
| title_full | Research on highway rain monitoring based on rain monitoring coefficient |
| title_fullStr | Research on highway rain monitoring based on rain monitoring coefficient |
| title_full_unstemmed | Research on highway rain monitoring based on rain monitoring coefficient |
| title_short | Research on highway rain monitoring based on rain monitoring coefficient |
| title_sort | research on highway rain monitoring based on rain monitoring coefficient |
| url | https://doi.org/10.1038/s41598-024-53360-1 |
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