UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
With the development of our society, unmanned aerial vehicles (UAVs) appear more frequently in people’s daily lives, which could become a threat to public security and privacy, especially at night. At the same time, laser active imaging is an important detection method for night vision. In this pape...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/5182 |
| _version_ | 1827690770939772928 |
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| author | Shao Zhang Guoqing Yang Tao Sun Kunyang Du Jin Guo |
| author_facet | Shao Zhang Guoqing Yang Tao Sun Kunyang Du Jin Guo |
| author_sort | Shao Zhang |
| collection | DOAJ |
| description | With the development of our society, unmanned aerial vehicles (UAVs) appear more frequently in people’s daily lives, which could become a threat to public security and privacy, especially at night. At the same time, laser active imaging is an important detection method for night vision. In this paper, we implement a UAV detection model for our laser active imaging system based on deep learning and a simulated dataset that we constructed. Firstly, the model is pre-trained on the largest available dataset. Then, it is transferred to a simulated dataset to learn about the UAV features. Finally, the trained model is tested on real laser active imaging data. The experimental results show that the performance of the proposed method is greatly improved compared to the model not trained on the simulated dataset, which verifies the transferability of features learned from the simulated data, the effectiveness of the proposed simulation method, and the feasibility of our solution for UAV detection in the laser active imaging domain. Furthermore, a comparative experiment with the previous method is carried out. The results show that our model can achieve high-precision, real-time detection at 104.1 frames per second (FPS). |
| first_indexed | 2024-03-10T10:45:45Z |
| format | Article |
| id | doaj.art-46eccc44da8f49b9ac8bccfb6ba07b2f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:45:45Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-46eccc44da8f49b9ac8bccfb6ba07b2f2023-11-21T22:35:37ZengMDPI AGApplied Sciences2076-34172021-06-011111518210.3390/app11115182UAV Detection with Transfer Learning from Simulated Data of Laser Active ImagingShao Zhang0Guoqing Yang1Tao Sun2Kunyang Du3Jin Guo4State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaVisual Computing Research Center, Shenzhen University, Shenzhen 518061, ChinaState Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaState Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaState Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaWith the development of our society, unmanned aerial vehicles (UAVs) appear more frequently in people’s daily lives, which could become a threat to public security and privacy, especially at night. At the same time, laser active imaging is an important detection method for night vision. In this paper, we implement a UAV detection model for our laser active imaging system based on deep learning and a simulated dataset that we constructed. Firstly, the model is pre-trained on the largest available dataset. Then, it is transferred to a simulated dataset to learn about the UAV features. Finally, the trained model is tested on real laser active imaging data. The experimental results show that the performance of the proposed method is greatly improved compared to the model not trained on the simulated dataset, which verifies the transferability of features learned from the simulated data, the effectiveness of the proposed simulation method, and the feasibility of our solution for UAV detection in the laser active imaging domain. Furthermore, a comparative experiment with the previous method is carried out. The results show that our model can achieve high-precision, real-time detection at 104.1 frames per second (FPS).https://www.mdpi.com/2076-3417/11/11/5182laser active imagingobject detectiontransfer learning |
| spellingShingle | Shao Zhang Guoqing Yang Tao Sun Kunyang Du Jin Guo UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging Applied Sciences laser active imaging object detection transfer learning |
| title | UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging |
| title_full | UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging |
| title_fullStr | UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging |
| title_full_unstemmed | UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging |
| title_short | UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging |
| title_sort | uav detection with transfer learning from simulated data of laser active imaging |
| topic | laser active imaging object detection transfer learning |
| url | https://www.mdpi.com/2076-3417/11/11/5182 |
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