Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos
Aging port infrastructure demands frequent and reliable inspections, yet the existing automated systems often require expensive industrial drones, posing significant adoption barriers for local governments with limited resources. To address this challenge, this study develops a low-cost, automated c...
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4325 |
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| author | Daiki Komi Daisuke Yoshida Tomohito Kameyama |
| author_facet | Daiki Komi Daisuke Yoshida Tomohito Kameyama |
| author_sort | Daiki Komi |
| collection | DOAJ |
| description | Aging port infrastructure demands frequent and reliable inspections, yet the existing automated systems often require expensive industrial drones, posing significant adoption barriers for local governments with limited resources. To address this challenge, this study develops a low-cost, automated crack detection system for port quay walls utilizing orthophotos generated from a small general-purpose drone. The system employs the YOLOR (You Only Learn One Representation) object detection algorithm, enhanced by two novel image processing techniques—overlapping tiling and pseudo-altitude slicing—to overcome the resolution limitations of low-cost cameras. While official guidelines for port facilities designate 3 mm as an inspection threshold, our system is specifically designed to achieve a higher-resolution detection capability for cracks as narrow as 1 mm. This approach ensures reliable detection with a sufficient safety margin and enables the proactive monitoring of crack progression for preventive maintenance. The effectiveness of the proposed image processing techniques was validated, with an <i>F</i><sub>1</sub> score-based analysis revealing key trade-offs between maximizing detection recall and achieving a balanced performance depending on the chosen simulated altitude. Furthermore, evaluation using real-world inspection data demonstrated that the proposed system achieves a detection performance comparable to that of a well-established commercial system, confirming its practical applicability. Crucially, by mapping the detected cracks to real-world coordinates on georeferenced orthophotos, the system provides a foundation for advanced, data-driven asset management, allowing for the quantitative tracking of deterioration over time. These results confirm that the proposed workflow is a practical and sustainable solution for infrastructure monitoring. |
| format | Article |
| id | doaj-art-13b0ae2b5a33421294a552e4a5612311 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-13b0ae2b5a33421294a552e4a56123112025-08-20T02:47:21ZengMDPI AGSensors1424-82202025-07-012514432510.3390/s25144325Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and OrthophotosDaiki Komi0Daisuke Yoshida1Tomohito Kameyama2Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, JapanGraduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, JapanGraduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, JapanAging port infrastructure demands frequent and reliable inspections, yet the existing automated systems often require expensive industrial drones, posing significant adoption barriers for local governments with limited resources. To address this challenge, this study develops a low-cost, automated crack detection system for port quay walls utilizing orthophotos generated from a small general-purpose drone. The system employs the YOLOR (You Only Learn One Representation) object detection algorithm, enhanced by two novel image processing techniques—overlapping tiling and pseudo-altitude slicing—to overcome the resolution limitations of low-cost cameras. While official guidelines for port facilities designate 3 mm as an inspection threshold, our system is specifically designed to achieve a higher-resolution detection capability for cracks as narrow as 1 mm. This approach ensures reliable detection with a sufficient safety margin and enables the proactive monitoring of crack progression for preventive maintenance. The effectiveness of the proposed image processing techniques was validated, with an <i>F</i><sub>1</sub> score-based analysis revealing key trade-offs between maximizing detection recall and achieving a balanced performance depending on the chosen simulated altitude. Furthermore, evaluation using real-world inspection data demonstrated that the proposed system achieves a detection performance comparable to that of a well-established commercial system, confirming its practical applicability. Crucially, by mapping the detected cracks to real-world coordinates on georeferenced orthophotos, the system provides a foundation for advanced, data-driven asset management, allowing for the quantitative tracking of deterioration over time. These results confirm that the proposed workflow is a practical and sustainable solution for infrastructure monitoring.https://www.mdpi.com/1424-8220/25/14/4325orthophotoport quay wallcrack detectiondeep learningsmall general-purpose drone |
| spellingShingle | Daiki Komi Daisuke Yoshida Tomohito Kameyama Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos Sensors orthophoto port quay wall crack detection deep learning small general-purpose drone |
| title | Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos |
| title_full | Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos |
| title_fullStr | Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos |
| title_full_unstemmed | Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos |
| title_short | Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos |
| title_sort | development of an automated crack detection system for port quay walls using a small general purpose drone and orthophotos |
| topic | orthophoto port quay wall crack detection deep learning small general-purpose drone |
| url | https://www.mdpi.com/1424-8220/25/14/4325 |
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