Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study
Manhole covers are crucial for maintaining urban operations and ensuring residents’ travel. The traditional inspection and maintenance management system based on manual judgment has low efficiency and poor accuracy, making it difficult to adapt to the rapidly expanding urban construction and complex...
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| Language: | English |
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4144 |
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| author | Yanqiong Ding Baojiang Han Hua Jiang Hao Hu Lei Xue Jiasen Weng Zhili Tang Yuzhang Liu |
| author_facet | Yanqiong Ding Baojiang Han Hua Jiang Hao Hu Lei Xue Jiasen Weng Zhili Tang Yuzhang Liu |
| author_sort | Yanqiong Ding |
| collection | DOAJ |
| description | Manhole covers are crucial for maintaining urban operations and ensuring residents’ travel. The traditional inspection and maintenance management system based on manual judgment has low efficiency and poor accuracy, making it difficult to adapt to the rapidly expanding urban construction and complex environment of manhole covers. To address these challenges, an intelligent management model based on the improved YOLOv8 model is proposed for three types of urban high-frequency defects: “breakage, loss and shift”. We design a lightweight dual-stream feature extraction network and use EfficientNetV2 as the backbone. By introducing the fused MBConv structure, the computational complexity is significantly reduced, while the efficiency of feature extraction is improved. An innovative foreground attention module is introduced to adaptively enhance the features of manhole cover defects, improving the model’s ability to identify defects of various scales. In addition, an optimized feature fusion architecture is constructed by integrating NAS-FPN modules. This structure utilizes bidirectional feature transfer and automatic structure search, significantly enhancing the expressiveness of multi-scale features. A combined loss function design using GIoU loss, dynamically weighted BCE loss, and Distribution Focal Loss (DFL) is adopted to address the issues of sample imbalance and inter-class differences. The experimental results show that the model achieved excellent performance in multiple indicators of manhole cover defect recognition, especially in classification accuracy, recall rate, and F1-score, with an overall recognition accuracy of 98.6%. The application of the improved model in the new smart management system for urban manhole covers can significantly improve management efficiency. |
| format | Article |
| id | doaj-art-3acedaab63e143cfb7dba671ef0a50ec |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3acedaab63e143cfb7dba671ef0a50ec2025-08-20T02:36:30ZengMDPI AGSensors1424-82202025-07-012513414410.3390/s25134144Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case StudyYanqiong Ding0Baojiang Han1Hua Jiang2Hao Hu3Lei Xue4Jiasen Weng5Zhili Tang6Yuzhang Liu7Beijing Information Infrastructure Construction Co., Ltd., Beijing 100080, ChinaBeijing Information Infrastructure Construction Co., Ltd., Beijing 100080, ChinaSchool of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing 100083, ChinaSchool of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing 100083, ChinaBeijing Information Infrastructure Construction Co., Ltd., Beijing 100080, ChinaSchool of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing 100083, ChinaBeijing Information Infrastructure Construction Co., Ltd., Beijing 100080, ChinaBeijing Information Infrastructure Construction Co., Ltd., Beijing 100080, ChinaManhole covers are crucial for maintaining urban operations and ensuring residents’ travel. The traditional inspection and maintenance management system based on manual judgment has low efficiency and poor accuracy, making it difficult to adapt to the rapidly expanding urban construction and complex environment of manhole covers. To address these challenges, an intelligent management model based on the improved YOLOv8 model is proposed for three types of urban high-frequency defects: “breakage, loss and shift”. We design a lightweight dual-stream feature extraction network and use EfficientNetV2 as the backbone. By introducing the fused MBConv structure, the computational complexity is significantly reduced, while the efficiency of feature extraction is improved. An innovative foreground attention module is introduced to adaptively enhance the features of manhole cover defects, improving the model’s ability to identify defects of various scales. In addition, an optimized feature fusion architecture is constructed by integrating NAS-FPN modules. This structure utilizes bidirectional feature transfer and automatic structure search, significantly enhancing the expressiveness of multi-scale features. A combined loss function design using GIoU loss, dynamically weighted BCE loss, and Distribution Focal Loss (DFL) is adopted to address the issues of sample imbalance and inter-class differences. The experimental results show that the model achieved excellent performance in multiple indicators of manhole cover defect recognition, especially in classification accuracy, recall rate, and F1-score, with an overall recognition accuracy of 98.6%. The application of the improved model in the new smart management system for urban manhole covers can significantly improve management efficiency.https://www.mdpi.com/1424-8220/25/13/4144manhole covermodel improvementlightweight networkattention modulemulti-scale feature fusion |
| spellingShingle | Yanqiong Ding Baojiang Han Hua Jiang Hao Hu Lei Xue Jiasen Weng Zhili Tang Yuzhang Liu Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study Sensors manhole cover model improvement lightweight network attention module multi-scale feature fusion |
| title | Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study |
| title_full | Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study |
| title_fullStr | Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study |
| title_full_unstemmed | Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study |
| title_short | Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study |
| title_sort | application of improved yolov8 image model in urban manhole cover defect management and detection case study |
| topic | manhole cover model improvement lightweight network attention module multi-scale feature fusion |
| url | https://www.mdpi.com/1424-8220/25/13/4144 |
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