An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection
Aircraft safety is the aviation industry’s primary concern. Inspections must be conducted before each flight to ensure the integrity of the aircraft. To meet the increasing demand for engineers, a system capable of detecting surface defects on aircraft was designed to reduce the workload of the insp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/1/31 |
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| author | Kuo-Chien Liao Jirayu Lau Muhamad Hidayat |
| author_facet | Kuo-Chien Liao Jirayu Lau Muhamad Hidayat |
| author_sort | Kuo-Chien Liao |
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| description | Aircraft safety is the aviation industry’s primary concern. Inspections must be conducted before each flight to ensure the integrity of the aircraft. To meet the increasing demand for engineers, a system capable of detecting surface defects on aircraft was designed to reduce the workload of the inspection process. The system utilizes the real-time object detection capabilities of the you only look once-version 9 (YOLO v9) algorithm, combined with imagery captured from an unmanned aerial vehicle (UAV)-based aerial platform. This results in a system capable of detecting defects such as cracks and dents on the aircraft’s surface, even in areas that are difficult to reach, such as the upper surfaces of the wings or the higher parts of the fuselage. With the introduction of a Real-Time Messaging Protocol (RTMP) server, the results can be monitored via artificial intelligence (AI) and Internet of Things (IoT) devices in real time for further evaluation. The experimental results confirmed an effective recognition of defects, with a mean average precision (mAP@0.5) of 0.842 for all classes, the highest score being 0.938 for dents and the lowest value 0.733 for the paint-off class. This study demonstrates the potential for developing image detection technology with AI for the aviation industry. |
| format | Article |
| id | doaj-art-04e9125b6a3f445db27b3a4e98959609 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-04e9125b6a3f445db27b3a4e989596092025-01-24T13:15:32ZengMDPI AGAerospace2226-43102025-01-011213110.3390/aerospace12010031An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote InspectionKuo-Chien Liao0Jirayu Lau1Muhamad Hidayat2Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413, TaiwanDepartment of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413, TaiwanDepartment of Mechanical Engineering, Sumbawa University of Technology, Sumbawa Besar 84371, IndonesiaAircraft safety is the aviation industry’s primary concern. Inspections must be conducted before each flight to ensure the integrity of the aircraft. To meet the increasing demand for engineers, a system capable of detecting surface defects on aircraft was designed to reduce the workload of the inspection process. The system utilizes the real-time object detection capabilities of the you only look once-version 9 (YOLO v9) algorithm, combined with imagery captured from an unmanned aerial vehicle (UAV)-based aerial platform. This results in a system capable of detecting defects such as cracks and dents on the aircraft’s surface, even in areas that are difficult to reach, such as the upper surfaces of the wings or the higher parts of the fuselage. With the introduction of a Real-Time Messaging Protocol (RTMP) server, the results can be monitored via artificial intelligence (AI) and Internet of Things (IoT) devices in real time for further evaluation. The experimental results confirmed an effective recognition of defects, with a mean average precision (mAP@0.5) of 0.842 for all classes, the highest score being 0.938 for dents and the lowest value 0.733 for the paint-off class. This study demonstrates the potential for developing image detection technology with AI for the aviation industry.https://www.mdpi.com/2226-4310/12/1/31artificial intelligenceyou only look once-version9Internet of Thingsreal-time detectionaircraft structural damage detection |
| spellingShingle | Kuo-Chien Liao Jirayu Lau Muhamad Hidayat An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection Aerospace artificial intelligence you only look once-version9 Internet of Things real-time detection aircraft structural damage detection |
| title | An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection |
| title_full | An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection |
| title_fullStr | An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection |
| title_full_unstemmed | An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection |
| title_short | An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection |
| title_sort | innovative aircraft skin damage assessment using you only look once version9 a real time material evaluation system for remote inspection |
| topic | artificial intelligence you only look once-version9 Internet of Things real-time detection aircraft structural damage detection |
| url | https://www.mdpi.com/2226-4310/12/1/31 |
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