UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments
This study presents a UAV-based Structural Health Monitoring (SHM) system that combines Lighthouse localization with a two-stage CNN architecture—AlexNet for crack classification and YOLOv4 for segmentation—to enable reliable crack detection and spatial mapping in GNSS-denied environments. This stu...
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| Format: | Article |
| Language: | English |
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Ital Publication
2025-06-01
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| Series: | HighTech and Innovation Journal |
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| Online Access: | https://hightechjournal.org/index.php/HIJ/article/view/1100 |
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| author | Timothy Scott C. Chu Joses Sorilla Alvin Y. Chua |
| author_facet | Timothy Scott C. Chu Joses Sorilla Alvin Y. Chua |
| author_sort | Timothy Scott C. Chu |
| collection | DOAJ |
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This study presents a UAV-based Structural Health Monitoring (SHM) system that combines Lighthouse localization with a two-stage CNN architecture—AlexNet for crack classification and YOLOv4 for segmentation—to enable reliable crack detection and spatial mapping in GNSS-denied environments. This study explores the effectiveness of this combination as a practical and computationally efficient solution for indoor SHM tasks. The UAV was deployed within a 1.5 m × 1.2 m × 1.2 m test volume to inspect synthetic cracks derived from Özgenel’s dataset, as well as a real-world wall crack. Two experiments were conducted: evaluating UAV localization accuracy and assessing the system’s ability to detect cracks and provide corresponding pose data. The system achieved a 1–2 cm margin of error in pose estimation, alongside 100% precision, 83.33% recall, and 91.89% accuracy in crack detection. This level of localization accuracy supports stable autonomous UAV flight and ensures that cracks are detected and spatially localized with minimal deviation. Beyond classification and segmentation, the system returns pose data tied to each detected crack, allowing users to identify defect locations precisely and use this information to guide inspection or maintenance tasks. Future work includes expanding the dataset, generalization, and evaluating scalability via multi-base station setups.
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| format | Article |
| id | doaj-art-48e8d7fc68f0432aafb57914ed7015b5 |
| institution | Kabale University |
| issn | 2723-9535 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | HighTech and Innovation Journal |
| spelling | doaj-art-48e8d7fc68f0432aafb57914ed7015b52025-08-20T03:57:40ZengItal PublicationHighTech and Innovation Journal2723-95352025-06-016210.28991/HIJ-2025-06-02-03UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied EnvironmentsTimothy Scott C. Chu0https://orcid.org/0000-0001-5775-1532Joses Sorilla1Alvin Y. Chua2https://orcid.org/0000-0003-0954-7291Department of Mechanical Engineering, De La Salle University, 2401 Taft Ave. Malate, ManilaDepartment of Mechanical Engineering, De La Salle University, 2401 Taft Ave. Malate, ManilaDepartment of Mechanical Engineering, De La Salle University, 2401 Taft Ave. Malate, Manila This study presents a UAV-based Structural Health Monitoring (SHM) system that combines Lighthouse localization with a two-stage CNN architecture—AlexNet for crack classification and YOLOv4 for segmentation—to enable reliable crack detection and spatial mapping in GNSS-denied environments. This study explores the effectiveness of this combination as a practical and computationally efficient solution for indoor SHM tasks. The UAV was deployed within a 1.5 m × 1.2 m × 1.2 m test volume to inspect synthetic cracks derived from Özgenel’s dataset, as well as a real-world wall crack. Two experiments were conducted: evaluating UAV localization accuracy and assessing the system’s ability to detect cracks and provide corresponding pose data. The system achieved a 1–2 cm margin of error in pose estimation, alongside 100% precision, 83.33% recall, and 91.89% accuracy in crack detection. This level of localization accuracy supports stable autonomous UAV flight and ensures that cracks are detected and spatially localized with minimal deviation. Beyond classification and segmentation, the system returns pose data tied to each detected crack, allowing users to identify defect locations precisely and use this information to guide inspection or maintenance tasks. Future work includes expanding the dataset, generalization, and evaluating scalability via multi-base station setups. https://hightechjournal.org/index.php/HIJ/article/view/1100Crack DetectionCrack SegmentationGNSS-Denied EnvironmentsLighthouse LocalizationStructural Health MonitoringTwo-Stage CNN Model |
| spellingShingle | Timothy Scott C. Chu Joses Sorilla Alvin Y. Chua UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments HighTech and Innovation Journal Crack Detection Crack Segmentation GNSS-Denied Environments Lighthouse Localization Structural Health Monitoring Two-Stage CNN Model |
| title | UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments |
| title_full | UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments |
| title_fullStr | UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments |
| title_full_unstemmed | UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments |
| title_short | UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments |
| title_sort | uav based structural health monitoring using a two stage cnn model with lighthouse localization in gnss denied environments |
| topic | Crack Detection Crack Segmentation GNSS-Denied Environments Lighthouse Localization Structural Health Monitoring Two-Stage CNN Model |
| url | https://hightechjournal.org/index.php/HIJ/article/view/1100 |
| work_keys_str_mv | AT timothyscottcchu uavbasedstructuralhealthmonitoringusingatwostagecnnmodelwithlighthouselocalizationingnssdeniedenvironments AT josessorilla uavbasedstructuralhealthmonitoringusingatwostagecnnmodelwithlighthouselocalizationingnssdeniedenvironments AT alvinychua uavbasedstructuralhealthmonitoringusingatwostagecnnmodelwithlighthouselocalizationingnssdeniedenvironments |