A Lightweight Small Target Detection Algorithm for UAV Platforms
The targets in the aerial view of UAVs are small, scenes are complex, and background noise is strong. Additionally, the low computational capability of UAVs is challenged when trying to meet the requirements of large neural networks. Therefore, a lightweight object detection algorithm tailored for U...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/12 |
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| author | Yanhui Lv Bo Tian Qichao Guo Deyu Zhang |
| author_facet | Yanhui Lv Bo Tian Qichao Guo Deyu Zhang |
| author_sort | Yanhui Lv |
| collection | DOAJ |
| description | The targets in the aerial view of UAVs are small, scenes are complex, and background noise is strong. Additionally, the low computational capability of UAVs is challenged when trying to meet the requirements of large neural networks. Therefore, a lightweight object detection algorithm tailored for UAV platforms, called RSG-YOLO, is proposed. The algorithm introduces an attention module constructed with receptive field attention and coordinate attention, which helps reduce background noise interference while improving long-range information dependency. It also introduces and refines a fine-grained downsampling structure to minimize the loss of target information during the downsampling process. A general efficient layer aggregation network enhances the base feature extraction module, improving gradient flow information. Additionally, a detection layer rich in small target information is added, while redundant large object detection layers are removed, achieving a lightweight design while enhancing detection accuracy. Experimental results show that, compared to the baseline algorithm, the improved algorithm increases the P, R, mAP@0.5, and mAP@0.5:0.95 by 6.9%, 7.2%, 8.4%, 5.8%, respectively, on the VisDrone 2019 dataset, and by 5.7%, 9%, 9.3%, 3.6%, respectively, on the TinyPerson dataset, while reducing the number of parameters by 23.3%. This significantly enhances the model’s detection performance and robustness, making it highly suitable for object detection tasks on low-computing-power UAV platforms. |
| format | Article |
| id | doaj-art-2e4027b9cc8f47a295b7e089973c920a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-2e4027b9cc8f47a295b7e089973c920a2025-01-10T13:14:09ZengMDPI AGApplied Sciences2076-34172024-12-011511210.3390/app15010012A Lightweight Small Target Detection Algorithm for UAV PlatformsYanhui Lv0Bo Tian1Qichao Guo2Deyu Zhang3College of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaCollege of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaCollege of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaCollege of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaThe targets in the aerial view of UAVs are small, scenes are complex, and background noise is strong. Additionally, the low computational capability of UAVs is challenged when trying to meet the requirements of large neural networks. Therefore, a lightweight object detection algorithm tailored for UAV platforms, called RSG-YOLO, is proposed. The algorithm introduces an attention module constructed with receptive field attention and coordinate attention, which helps reduce background noise interference while improving long-range information dependency. It also introduces and refines a fine-grained downsampling structure to minimize the loss of target information during the downsampling process. A general efficient layer aggregation network enhances the base feature extraction module, improving gradient flow information. Additionally, a detection layer rich in small target information is added, while redundant large object detection layers are removed, achieving a lightweight design while enhancing detection accuracy. Experimental results show that, compared to the baseline algorithm, the improved algorithm increases the P, R, mAP@0.5, and mAP@0.5:0.95 by 6.9%, 7.2%, 8.4%, 5.8%, respectively, on the VisDrone 2019 dataset, and by 5.7%, 9%, 9.3%, 3.6%, respectively, on the TinyPerson dataset, while reducing the number of parameters by 23.3%. This significantly enhances the model’s detection performance and robustness, making it highly suitable for object detection tasks on low-computing-power UAV platforms.https://www.mdpi.com/2076-3417/15/1/12unmanned aerial vehiclesmall target detectionYOLOv8receptive field coordinated attentionfine-grained downsampling |
| spellingShingle | Yanhui Lv Bo Tian Qichao Guo Deyu Zhang A Lightweight Small Target Detection Algorithm for UAV Platforms Applied Sciences unmanned aerial vehicle small target detection YOLOv8 receptive field coordinated attention fine-grained downsampling |
| title | A Lightweight Small Target Detection Algorithm for UAV Platforms |
| title_full | A Lightweight Small Target Detection Algorithm for UAV Platforms |
| title_fullStr | A Lightweight Small Target Detection Algorithm for UAV Platforms |
| title_full_unstemmed | A Lightweight Small Target Detection Algorithm for UAV Platforms |
| title_short | A Lightweight Small Target Detection Algorithm for UAV Platforms |
| title_sort | lightweight small target detection algorithm for uav platforms |
| topic | unmanned aerial vehicle small target detection YOLOv8 receptive field coordinated attention fine-grained downsampling |
| url | https://www.mdpi.com/2076-3417/15/1/12 |
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