Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
The current infrastructure is crucial to metropolitan improvement. Natural factors, aging, and overuse cause these structures to deteriorate, introducing dangers to public well-being. Timely detection of infrastructure failures requires an effective solution. A YOLOv11-based deep learning model has...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/5/187 |
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| author | Raiyen Z. Rakin Mahmudur Rahman Kanij F. Borsa Fahmid Al Farid Shakila Rahman Jia Uddin Hezerul Abdul Karim |
| author_facet | Raiyen Z. Rakin Mahmudur Rahman Kanij F. Borsa Fahmid Al Farid Shakila Rahman Jia Uddin Hezerul Abdul Karim |
| author_sort | Raiyen Z. Rakin |
| collection | DOAJ |
| description | The current infrastructure is crucial to metropolitan improvement. Natural factors, aging, and overuse cause these structures to deteriorate, introducing dangers to public well-being. Timely detection of infrastructure failures requires an effective solution. A YOLOv11-based deep learning model has been proposed which analyzes infrastructure and detects faults in civil architecture. The focus of this study is on an image-based approach to infrastructure assessment, which is an alternative to manual visual inspections. Despite not explicitly modeling infrastructure deterioration, the proposed method is designed to automate defect identification based on visual cues. A customized dataset was created with 9116 images collected from various platforms. The dataset was pre-processed, i.e., annotated, and after pre-processing, the proposed model was trained. After training, our proposed model finds defects with greater precision and speed than conventional defect detection techniques. It achieves high performance with precision, recall, F1 score, and mAP in 100 epochs, and is therefore reliable for applications in civil engineering and urban infrastructure monitoring. Finally, the detection results show that the proposed YOLOv11 model works better than other baseline algorithms (YOLOv8, YOLOv9, and YOLOv10) and is more accurate at finding infrastructure problems in real-world scenarios. |
| format | Article |
| id | doaj-art-954f59ea7f6a4f2fbcd34968baece24e |
| institution | OA Journals |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-954f59ea7f6a4f2fbcd34968baece24e2025-08-20T02:33:48ZengMDPI AGFuture Internet1999-59032025-04-0117518710.3390/fi17050187Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11Raiyen Z. Rakin0Mahmudur Rahman1Kanij F. Borsa2Fahmid Al Farid3Shakila Rahman4Jia Uddin5Hezerul Abdul Karim6Department of Computer Science, American International University—Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science, American International University—Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science, American International University—Bangladesh, Dhaka 1229, BangladeshCentre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, MalaysiaDepartment of Computer Science, American International University—Bangladesh, Dhaka 1229, BangladeshAI and Big Data Department, Woosong University, Daejeon 34606, Republic of KoreaCentre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, MalaysiaThe current infrastructure is crucial to metropolitan improvement. Natural factors, aging, and overuse cause these structures to deteriorate, introducing dangers to public well-being. Timely detection of infrastructure failures requires an effective solution. A YOLOv11-based deep learning model has been proposed which analyzes infrastructure and detects faults in civil architecture. The focus of this study is on an image-based approach to infrastructure assessment, which is an alternative to manual visual inspections. Despite not explicitly modeling infrastructure deterioration, the proposed method is designed to automate defect identification based on visual cues. A customized dataset was created with 9116 images collected from various platforms. The dataset was pre-processed, i.e., annotated, and after pre-processing, the proposed model was trained. After training, our proposed model finds defects with greater precision and speed than conventional defect detection techniques. It achieves high performance with precision, recall, F1 score, and mAP in 100 epochs, and is therefore reliable for applications in civil engineering and urban infrastructure monitoring. Finally, the detection results show that the proposed YOLOv11 model works better than other baseline algorithms (YOLOv8, YOLOv9, and YOLOv10) and is more accurate at finding infrastructure problems in real-world scenarios.https://www.mdpi.com/1999-5903/17/5/187infrastructure fault detectioncomputer visionYOLOv11-based modelyou only look oncereal-time object detectiondata visualization |
| spellingShingle | Raiyen Z. Rakin Mahmudur Rahman Kanij F. Borsa Fahmid Al Farid Shakila Rahman Jia Uddin Hezerul Abdul Karim Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11 Future Internet infrastructure fault detection computer vision YOLOv11-based model you only look once real-time object detection data visualization |
| title | Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11 |
| title_full | Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11 |
| title_fullStr | Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11 |
| title_full_unstemmed | Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11 |
| title_short | Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11 |
| title_sort | towards safer cities ai powered infrastructure fault detection based on yolov11 |
| topic | infrastructure fault detection computer vision YOLOv11-based model you only look once real-time object detection data visualization |
| url | https://www.mdpi.com/1999-5903/17/5/187 |
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