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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2023-01-01
|
| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.06.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1001-9235 |