A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11
Research on anti-UAV (anti-unmanned aerial vehicle) detection techniques is essential, since the widespread use of UAVs, while improving convenience, poses several hidden risks to privacy, security, air control, etc. This paper focuses on the challenges of long-distance UAV identification and propos...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/11 |
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| author | Yunlong Gao Yibing Xin Huan Yang Yongjuan Wang |
| author_facet | Yunlong Gao Yibing Xin Huan Yang Yongjuan Wang |
| author_sort | Yunlong Gao |
| collection | DOAJ |
| description | Research on anti-UAV (anti-unmanned aerial vehicle) detection techniques is essential, since the widespread use of UAVs, while improving convenience, poses several hidden risks to privacy, security, air control, etc. This paper focuses on the challenges of long-distance UAV identification and proposes a lightweight anti-UAV detection method based on improved YOLOv11. Firstly, HWD is imported as the backbone’s downsampling module, which lowers feature loss in the feature extraction procedure while using fewer parameters. A lighter CCFM structure is then used in place of the original neck portion, to improve the model’s capacity to detect small targets and adjust to scale changes. The detection effect on small targets is greatly enhanced by removing the original large-scale detection head and adding a new small-scale detection head in response to the small size of UAV targets. In this paper, experimental validation was carried out using the DUT ANTI-UAV dataset, and, compared to the baseline model YOLOv11, the method we propose improved the P, R, mAP50, and mAP50-05 metrics by 4%, 4.5%, 4.1%, and 4.9%, respectively, and decreased the parameters by 38.4%. However, the FPS declined by roughly 5%. The experimental results show that the improved method we propose has better performance in anti-UAV detection tasks, and the model is more lightweight. |
| format | Article |
| id | doaj-art-4fa725cf7d3c449592fce09251206454 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-4fa725cf7d3c449592fce092512064542025-01-24T13:29:38ZengMDPI AGDrones2504-446X2024-12-01911110.3390/drones9010011A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11Yunlong Gao0Yibing Xin1Huan Yang2Yongjuan Wang3School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaHangzhou Zhiyuan Research Ltd., Hangzhou 310014, ChinaNORINCO Group Air Ammunition Research Institute Co., Ltd., Haerbin 150030, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaResearch on anti-UAV (anti-unmanned aerial vehicle) detection techniques is essential, since the widespread use of UAVs, while improving convenience, poses several hidden risks to privacy, security, air control, etc. This paper focuses on the challenges of long-distance UAV identification and proposes a lightweight anti-UAV detection method based on improved YOLOv11. Firstly, HWD is imported as the backbone’s downsampling module, which lowers feature loss in the feature extraction procedure while using fewer parameters. A lighter CCFM structure is then used in place of the original neck portion, to improve the model’s capacity to detect small targets and adjust to scale changes. The detection effect on small targets is greatly enhanced by removing the original large-scale detection head and adding a new small-scale detection head in response to the small size of UAV targets. In this paper, experimental validation was carried out using the DUT ANTI-UAV dataset, and, compared to the baseline model YOLOv11, the method we propose improved the P, R, mAP50, and mAP50-05 metrics by 4%, 4.5%, 4.1%, and 4.9%, respectively, and decreased the parameters by 38.4%. However, the FPS declined by roughly 5%. The experimental results show that the improved method we propose has better performance in anti-UAV detection tasks, and the model is more lightweight.https://www.mdpi.com/2504-446X/9/1/11anti-UAVsmall target detectionYOLOv11HWDCCFM |
| spellingShingle | Yunlong Gao Yibing Xin Huan Yang Yongjuan Wang A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 Drones anti-UAV small target detection YOLOv11 HWD CCFM |
| title | A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 |
| title_full | A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 |
| title_fullStr | A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 |
| title_full_unstemmed | A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 |
| title_short | A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 |
| title_sort | lightweight anti unmanned aerial vehicle detection method based on improved yolov11 |
| topic | anti-UAV small target detection YOLOv11 HWD CCFM |
| url | https://www.mdpi.com/2504-446X/9/1/11 |
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