YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility
Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, en...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/3/179 |
| _version_ | 1797242472345108480 |
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| author | Nan Lao Ywet Aye Aye Maw Tuan Anh Nguyen Jae-Woo Lee |
| author_facet | Nan Lao Ywet Aye Aye Maw Tuan Anh Nguyen Jae-Woo Lee |
| author_sort | Nan Lao Ywet |
| collection | DOAJ |
| description | Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework’s objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM. |
| first_indexed | 2024-04-24T18:39:46Z |
| format | Article |
| id | doaj.art-53ccfd18e4974419a71303e4b734107a |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-04-24T18:39:46Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-53ccfd18e4974419a71303e4b734107a2024-03-27T13:15:35ZengMDPI AGAerospace2226-43102024-02-0111317910.3390/aerospace11030179YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial MobilityNan Lao Ywet0Aye Aye Maw1Tuan Anh Nguyen2Jae-Woo Lee3Department of Aerospace Information Engineering, Konkuk University, Seoul 05029, Republic of KoreaKonkuk Aerospace Design-Airworthiness Institute (KADA), Konkuk University, Seoul 05029, Republic of KoreaKonkuk Aerospace Design-Airworthiness Institute (KADA), Konkuk University, Seoul 05029, Republic of KoreaDepartment of Aerospace Information Engineering, Konkuk University, Seoul 05029, Republic of KoreaUrban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework’s objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM.https://www.mdpi.com/2226-4310/11/3/179urbanair mobilityoperational digital twincollision detectionsituation awarenessdeep learningtransfer learning |
| spellingShingle | Nan Lao Ywet Aye Aye Maw Tuan Anh Nguyen Jae-Woo Lee YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility Aerospace urbanair mobility operational digital twin collision detection situation awareness deep learning transfer learning |
| title | YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility |
| title_full | YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility |
| title_fullStr | YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility |
| title_full_unstemmed | YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility |
| title_short | YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility |
| title_sort | yolotransfer dt an operational digital twin framework with deep and transfer learning for collision detection and situation awareness in urban aerial mobility |
| topic | urbanair mobility operational digital twin collision detection situation awareness deep learning transfer learning |
| url | https://www.mdpi.com/2226-4310/11/3/179 |
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