Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse Detection
In order to solve the problem that the traditional urban waterlogging monitoring methods consume a lot of manpower and material resources and also have a high cost,which cannot meet the needs of comprehensive and rapid monitoring of urban flood,a method of monitoring the depth of urban road waterlog...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2023-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.06.001 |
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| author | LIAO Yuhong HUANG Guoru |
| author_facet | LIAO Yuhong HUANG Guoru |
| author_sort | LIAO Yuhong |
| collection | DOAJ |
| description | In order to solve the problem that the traditional urban waterlogging monitoring methods consume a lot of manpower and material resources and also have a high cost,which cannot meet the needs of comprehensive and rapid monitoring of urban flood,a method of monitoring the depth of urban road waterlogging using deep learning and ellipse detection algorithm is applied,which constructs a computation model of the urban road waterlogging depth by detecting and segmenting the wheels of different types of vehicles on the images through a deep learning model and using ellipse detection algorithm to extract the geometric characteristic parameters of submerged wheels.Verified by typical video monitoring sites in Dongying City,The results show that the average positioning precision and segmentation precision of the model on the dataset can reach over 94%;the model has a good monitoring effect on waterlogging depth for vehicles on both the directly lateral side and the oblique lateral side in actual waterlogging monitoring;furthermore,the results at the near point are better than that at the far point,and the results for vehicles on the directly lateral side are better than the oblique lateral side.The results can lay the foundation for further research,and provide technical support for urban waterlogging monitoring and flood emergency management. |
| format | Article |
| id | doaj-art-d929a399bfb14d93838620fdbafec1c3 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-d929a399bfb14d93838620fdbafec1c32025-01-15T02:21:28ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352023-01-014447628151Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse DetectionLIAO YuhongHUANG GuoruIn order to solve the problem that the traditional urban waterlogging monitoring methods consume a lot of manpower and material resources and also have a high cost,which cannot meet the needs of comprehensive and rapid monitoring of urban flood,a method of monitoring the depth of urban road waterlogging using deep learning and ellipse detection algorithm is applied,which constructs a computation model of the urban road waterlogging depth by detecting and segmenting the wheels of different types of vehicles on the images through a deep learning model and using ellipse detection algorithm to extract the geometric characteristic parameters of submerged wheels.Verified by typical video monitoring sites in Dongying City,The results show that the average positioning precision and segmentation precision of the model on the dataset can reach over 94%;the model has a good monitoring effect on waterlogging depth for vehicles on both the directly lateral side and the oblique lateral side in actual waterlogging monitoring;furthermore,the results at the near point are better than that at the far point,and the results for vehicles on the directly lateral side are better than the oblique lateral side.The results can lay the foundation for further research,and provide technical support for urban waterlogging monitoring and flood emergency management.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.06.001deep learningellipse detectionwaterlogging depthurban waterloggingimage recognition |
| spellingShingle | LIAO Yuhong HUANG Guoru Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse Detection Renmin Zhujiang deep learning ellipse detection waterlogging depth urban waterlogging image recognition |
| title | Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse Detection |
| title_full | Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse Detection |
| title_fullStr | Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse Detection |
| title_full_unstemmed | Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse Detection |
| title_short | Research on Monitoring method of Urban Road Waterlogging Depth Based on Deep Learning and Ellipse Detection |
| title_sort | research on monitoring method of urban road waterlogging depth based on deep learning and ellipse detection |
| topic | deep learning ellipse detection waterlogging depth urban waterlogging image recognition |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.06.001 |
| work_keys_str_mv | AT liaoyuhong researchonmonitoringmethodofurbanroadwaterloggingdepthbasedondeeplearningandellipsedetection AT huangguoru researchonmonitoringmethodofurbanroadwaterloggingdepthbasedondeeplearningandellipsedetection |