Monitoring Method for Road Waterlogging Based on Improved YOLOv8
Flooding disasters occur frequently in China, especially on roads, which seriously affect people's normal travel and even threaten their lives. The current technologies for monitoring road waterlogging are inefficient, and there is an urgent need for an efficient method to monitor road waterlog...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2024-10-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.10.005 |
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| _version_ | 1841530550822633472 |
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| author | ZHANG Zheng ZUO Xiangyang LONG Yan HUANG Haocheng HE Lixin LEI Xiaohui WANG Mengqian |
| author_facet | ZHANG Zheng ZUO Xiangyang LONG Yan HUANG Haocheng HE Lixin LEI Xiaohui WANG Mengqian |
| author_sort | ZHANG Zheng |
| collection | DOAJ |
| description | Flooding disasters occur frequently in China, especially on roads, which seriously affect people's normal travel and even threaten their lives. The current technologies for monitoring road waterlogging are inefficient, and there is an urgent need for an efficient method to monitor road waterlogging. Accurate monitoring of road waterlogging is helpful for the government to issue policies and personnel to take preventive measures. Therefore, this article proposed a real-time monitoring method for road waterlogging based on improved YOLOv8. Through the YOLOv8 algorithm, a convolutional block attention module (CBAM) attention mechanism was added to the neck structure network to enhance the important features of waterlogging areas, suppress general features, and improve the accuracy of identifying road waterlogging. In addition, perspective transformation and pixels were used to calculate the waterlogging area. The article studied the road waterlogging in the new campus of Hebei University of Engineering. The results show that the accuracy of this method reaches 93.83%, which can accurately identify the road waterlogging surface and output the waterlogging area in real time, meeting the monitoring needs. |
| format | Article |
| id | doaj-art-d923f271aee6437e8eefa0f93516afd9 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-d923f271aee6437e8eefa0f93516afd92025-01-15T03:02:13ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-10-0145445062797729Monitoring Method for Road Waterlogging Based on Improved YOLOv8ZHANG ZhengZUO XiangyangLONG YanHUANG HaochengHE LixinLEI XiaohuiWANG MengqianFlooding disasters occur frequently in China, especially on roads, which seriously affect people's normal travel and even threaten their lives. The current technologies for monitoring road waterlogging are inefficient, and there is an urgent need for an efficient method to monitor road waterlogging. Accurate monitoring of road waterlogging is helpful for the government to issue policies and personnel to take preventive measures. Therefore, this article proposed a real-time monitoring method for road waterlogging based on improved YOLOv8. Through the YOLOv8 algorithm, a convolutional block attention module (CBAM) attention mechanism was added to the neck structure network to enhance the important features of waterlogging areas, suppress general features, and improve the accuracy of identifying road waterlogging. In addition, perspective transformation and pixels were used to calculate the waterlogging area. The article studied the road waterlogging in the new campus of Hebei University of Engineering. The results show that the accuracy of this method reaches 93.83%, which can accurately identify the road waterlogging surface and output the waterlogging area in real time, meeting the monitoring needs.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.10.005waterlogging disasterdeep learningattention mechanismroad waterlogging |
| spellingShingle | ZHANG Zheng ZUO Xiangyang LONG Yan HUANG Haocheng HE Lixin LEI Xiaohui WANG Mengqian Monitoring Method for Road Waterlogging Based on Improved YOLOv8 Renmin Zhujiang waterlogging disaster deep learning attention mechanism road waterlogging |
| title | Monitoring Method for Road Waterlogging Based on Improved YOLOv8 |
| title_full | Monitoring Method for Road Waterlogging Based on Improved YOLOv8 |
| title_fullStr | Monitoring Method for Road Waterlogging Based on Improved YOLOv8 |
| title_full_unstemmed | Monitoring Method for Road Waterlogging Based on Improved YOLOv8 |
| title_short | Monitoring Method for Road Waterlogging Based on Improved YOLOv8 |
| title_sort | monitoring method for road waterlogging based on improved yolov8 |
| topic | waterlogging disaster deep learning attention mechanism road waterlogging |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.10.005 |
| work_keys_str_mv | AT zhangzheng monitoringmethodforroadwaterloggingbasedonimprovedyolov8 AT zuoxiangyang monitoringmethodforroadwaterloggingbasedonimprovedyolov8 AT longyan monitoringmethodforroadwaterloggingbasedonimprovedyolov8 AT huanghaocheng monitoringmethodforroadwaterloggingbasedonimprovedyolov8 AT helixin monitoringmethodforroadwaterloggingbasedonimprovedyolov8 AT leixiaohui monitoringmethodforroadwaterloggingbasedonimprovedyolov8 AT wangmengqian monitoringmethodforroadwaterloggingbasedonimprovedyolov8 |