Foreign object debris detection in lane images using deep learning methodology
Background Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-relate...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2570.pdf |
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| author | Priyadharsini S. Bhuvaneshwara Raja K. Kousi Krishnan T. Senthil Kumar Jagatheesaperumal Bader Fahad Alkhamees Mohammad Mehedi Hassan |
| author_facet | Priyadharsini S. Bhuvaneshwara Raja K. Kousi Krishnan T. Senthil Kumar Jagatheesaperumal Bader Fahad Alkhamees Mohammad Mehedi Hassan |
| author_sort | Priyadharsini S. |
| collection | DOAJ |
| description | Background Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-related damage costs over $4 billion annually, affecting airlines, airport tenants, and passengers. Current FOD clearance involves high-cost radars and significant manpower, and existing radar and camera-based surveillance methods are expensive to install. Methods This work proposes a video-based deep learning methodology to address the high cost of radar-based FOD detection. The proposed system consists of two modules for FOD detection: object classification and object localization. The classification module categorizes FOD into specific types of foreign objects. In the object localization module, these classified objects are pinpointed in video frames. Results The proposed system was experimentally tested with a large video dataset and compared with existing methods. The results demonstrated improved accuracy and robustness, allowing the FOD clearance team to quickly detect and remove foreign objects, thereby enhancing the safety and efficiency of airport runway operations. |
| format | Article |
| id | doaj-art-0e24cead2abe4e0398ddaea291e26162 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-0e24cead2abe4e0398ddaea291e261622025-01-23T15:05:11ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e257010.7717/peerj-cs.2570Foreign object debris detection in lane images using deep learning methodologyPriyadharsini S.0Bhuvaneshwara Raja K.1Kousi Krishnan T.2Senthil Kumar Jagatheesaperumal3Bader Fahad Alkhamees4Mohammad Mehedi Hassan5Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IndiaDepartment of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IndiaDepartment of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IndiaDepartment of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IndiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaBackground Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-related damage costs over $4 billion annually, affecting airlines, airport tenants, and passengers. Current FOD clearance involves high-cost radars and significant manpower, and existing radar and camera-based surveillance methods are expensive to install. Methods This work proposes a video-based deep learning methodology to address the high cost of radar-based FOD detection. The proposed system consists of two modules for FOD detection: object classification and object localization. The classification module categorizes FOD into specific types of foreign objects. In the object localization module, these classified objects are pinpointed in video frames. Results The proposed system was experimentally tested with a large video dataset and compared with existing methods. The results demonstrated improved accuracy and robustness, allowing the FOD clearance team to quickly detect and remove foreign objects, thereby enhancing the safety and efficiency of airport runway operations.https://peerj.com/articles/cs-2570.pdfConvolutional neural networkForeign object debrisObject detectionObject classificationAdaptive contour ROI |
| spellingShingle | Priyadharsini S. Bhuvaneshwara Raja K. Kousi Krishnan T. Senthil Kumar Jagatheesaperumal Bader Fahad Alkhamees Mohammad Mehedi Hassan Foreign object debris detection in lane images using deep learning methodology PeerJ Computer Science Convolutional neural network Foreign object debris Object detection Object classification Adaptive contour ROI |
| title | Foreign object debris detection in lane images using deep learning methodology |
| title_full | Foreign object debris detection in lane images using deep learning methodology |
| title_fullStr | Foreign object debris detection in lane images using deep learning methodology |
| title_full_unstemmed | Foreign object debris detection in lane images using deep learning methodology |
| title_short | Foreign object debris detection in lane images using deep learning methodology |
| title_sort | foreign object debris detection in lane images using deep learning methodology |
| topic | Convolutional neural network Foreign object debris Object detection Object classification Adaptive contour ROI |
| url | https://peerj.com/articles/cs-2570.pdf |
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