Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery
Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover ser...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/2769 |
| _version_ | 1797212945889886208 |
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| author | Dejiang Wang Yuping Huang |
| author_facet | Dejiang Wang Yuping Huang |
| author_sort | Dejiang Wang |
| collection | DOAJ |
| description | Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition. |
| first_indexed | 2024-04-24T10:50:27Z |
| format | Article |
| id | doaj.art-e87177186baf42dba9a9ecb1688146dd |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:50:27Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e87177186baf42dba9a9ecb1688146dd2024-04-12T13:14:46ZengMDPI AGApplied Sciences2076-34172024-03-01147276910.3390/app14072769Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial ImageryDejiang Wang0Yuping Huang1School of Mechanics and Engineering Science, Shanghai University, Shanghai 200444, ChinaSchool of Mechanics and Engineering Science, Shanghai University, Shanghai 200444, ChinaUrban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.https://www.mdpi.com/2076-3417/14/7/2769image super-resolution reconstructionmanhole cover recognitionmanhole cover positioningdrone aerial images |
| spellingShingle | Dejiang Wang Yuping Huang Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery Applied Sciences image super-resolution reconstruction manhole cover recognition manhole cover positioning drone aerial images |
| title | Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery |
| title_full | Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery |
| title_fullStr | Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery |
| title_full_unstemmed | Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery |
| title_short | Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery |
| title_sort | manhole cover classification based on super resolution reconstruction of unmanned aerial vehicle aerial imagery |
| topic | image super-resolution reconstruction manhole cover recognition manhole cover positioning drone aerial images |
| url | https://www.mdpi.com/2076-3417/14/7/2769 |
| work_keys_str_mv | AT dejiangwang manholecoverclassificationbasedonsuperresolutionreconstructionofunmannedaerialvehicleaerialimagery AT yupinghuang manholecoverclassificationbasedonsuperresolutionreconstructionofunmannedaerialvehicleaerialimagery |