Small Target Recognition and Tracking Based on UAV Platform
Target recognition and tracking based on multi-rotor UAVs have the advantages of low cost and high flexibility. It can monitor low-altitude targets with high intensity. It has great application prospects in national defense, military, and civil fields. The existing algorithms for aerial small target...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/17/6579 |
| _version_ | 1797493094684295168 |
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| author | Xiangrui Tian Yinjun Jia Xin Luo Jie Yin |
| author_facet | Xiangrui Tian Yinjun Jia Xin Luo Jie Yin |
| author_sort | Xiangrui Tian |
| collection | DOAJ |
| description | Target recognition and tracking based on multi-rotor UAVs have the advantages of low cost and high flexibility. It can monitor low-altitude targets with high intensity. It has great application prospects in national defense, military, and civil fields. The existing algorithms for aerial small target recognition and tracking have the disadvantages of slow speed, low accuracy, poor robustness, and insufficient intelligence. Aiming at the problems of existing algorithms, this paper first makes a lightweight improvement for the YOLOv4 network recognition algorithm suitable for small target recognition and tests it on the VisDrone dataset. The accuracy of the improved algorithm is increased by 1.5% and the speed is increased by 3.3 times. Then, by analyzing the response value, the KCF tracking situation is judged, and the template update of the adaptive learning rate is realized. When the tracking fails, the target is re-searched and tracked based on the recognition results and the similarity judgment. Finally, experiments are carried out on the multi-rotor UAV, and the adaptive zoom tracking strategy is designed to track pedestrians, cars, and UAVs. The results show that the proposed algorithm can achieve stable tracking of long-distance small targets. |
| first_indexed | 2024-03-10T01:15:05Z |
| format | Article |
| id | doaj.art-515e6fb9949d40e19ebd2c9c2472d6bd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:15:05Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-515e6fb9949d40e19ebd2c9c2472d6bd2023-11-23T14:10:52ZengMDPI AGSensors1424-82202022-08-012217657910.3390/s22176579Small Target Recognition and Tracking Based on UAV PlatformXiangrui Tian0Yinjun Jia1Xin Luo2Jie Yin3College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaTarget recognition and tracking based on multi-rotor UAVs have the advantages of low cost and high flexibility. It can monitor low-altitude targets with high intensity. It has great application prospects in national defense, military, and civil fields. The existing algorithms for aerial small target recognition and tracking have the disadvantages of slow speed, low accuracy, poor robustness, and insufficient intelligence. Aiming at the problems of existing algorithms, this paper first makes a lightweight improvement for the YOLOv4 network recognition algorithm suitable for small target recognition and tests it on the VisDrone dataset. The accuracy of the improved algorithm is increased by 1.5% and the speed is increased by 3.3 times. Then, by analyzing the response value, the KCF tracking situation is judged, and the template update of the adaptive learning rate is realized. When the tracking fails, the target is re-searched and tracked based on the recognition results and the similarity judgment. Finally, experiments are carried out on the multi-rotor UAV, and the adaptive zoom tracking strategy is designed to track pedestrians, cars, and UAVs. The results show that the proposed algorithm can achieve stable tracking of long-distance small targets.https://www.mdpi.com/1424-8220/22/17/6579intelligent recognitiontarget trackingdeep learningmulti-rotor UAV |
| spellingShingle | Xiangrui Tian Yinjun Jia Xin Luo Jie Yin Small Target Recognition and Tracking Based on UAV Platform Sensors intelligent recognition target tracking deep learning multi-rotor UAV |
| title | Small Target Recognition and Tracking Based on UAV Platform |
| title_full | Small Target Recognition and Tracking Based on UAV Platform |
| title_fullStr | Small Target Recognition and Tracking Based on UAV Platform |
| title_full_unstemmed | Small Target Recognition and Tracking Based on UAV Platform |
| title_short | Small Target Recognition and Tracking Based on UAV Platform |
| title_sort | small target recognition and tracking based on uav platform |
| topic | intelligent recognition target tracking deep learning multi-rotor UAV |
| url | https://www.mdpi.com/1424-8220/22/17/6579 |
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