Infrared and Laser Engineering, Volume. 53, Issue 1, 20230429(2024)
Structure characteristics sensing method of unmanned aerial vehicle group based on infrared detection
Figures & Tables(22)
Fig. 2. Structure of space to depth-channel attention net at
Fig. 10. Detection results of GMR-YOLOv5 algorithm in three interference cases
Fig. 11. Detection results of YOLOv5 algorithm in three interference cases
Fig. 12. Detection results of YOLOv7 algorithm in three interference cases
Fig. 13. Detection results of YOLOX algorithm in three interference cases
Table 1. Dataset information
View tableView in ArticleTable 1. Dataset information
Title Norm Dataset name Drone-swarms Dataset Number of images 6900 sheet Resolution of an image/pixel 960×540 Number of UAV 20-25 racks UAV target size/pixel 9×8-13×11
Table 2. Configuration of the experimental platform
View tableView in ArticleTable 2. Configuration of the experimental platform
Title Norm Operating system Ubuntu 18.04 Processing unit Intel Xeon Gold 6230×2 Graphics board NVIDIA RTX 8000 ×2 RAM 192 G（32 G×6） DDR4 2933 MT/s Development environment Python 3.6 PyTorch 1.10.0 CUDA 11.4
Table 3. Parameter settings
View tableView in ArticleTable 3. Parameter settings
Parameter name Parameter size Epoch 300 Attenuation factor 0.0005 Learning rate 0.001 Model optimizer Adam Batch size 8
Table 4. Comparative experimental results
View tableView in ArticleTable 4. Comparative experimental results
Algorithms mAP@0.50 mAP@0.50:0.95 FPS GMR-YOLOv5 95.9% 70.1% 59 YOLOv5 89.6% 60.5% 67 YOLOv7 86.3% 28.9% 40 YOLOX 85.9% 28.8% 33 SSD 56.7% 23.2% 87 Faster R-CNN 58.3% 30.1% 16
Table 5. Information about the dataset for the three disturbance scenarios
View tableView in ArticleTable 5. Information about the dataset for the three disturbance scenarios
Title Norm Motion crossover 606 sheet Motion blur 564 sheet Dense distribution 523 sheet UAV target size/pixel 9×8-13×11
Table 6. Comparison of experimental results in the case of motion crossover
View tableView in ArticleTable 6. Comparison of experimental results in the case of motion crossover
Algorithms mAP@0.50 mAP@0.50:0.95 FPS GMR-YOLOv5 82.2% 43.4% 60 YOLOv5 58.8% 15.1% 69 YOLOv7 69.1% 16.2% 21 YOLOX 75.9% 17.2% 30
Table 7. Comparison of experimental results in the case of motion blur
View tableView in ArticleTable 7. Comparison of experimental results in the case of motion blur
Algorithms mAP@0.50 mAP@0.50:0.95 FPS GMR-YOLOv5 91.3% 56.2% 60 YOLOv5 87.3% 26.9% 69 YOLOv7 86.1% 26% 23 YOLOX 88% 24.8% 30
Table 8. Comparison of experimental results in the case of dense distribution
View tableView in ArticleTable 8. Comparison of experimental results in the case of dense distribution
Algorithms mAP@0.50 mAP@0.50:0.95 FPS GMR-YOLOv5 90.9% 55.5% 60 YOLOv5 75.5% 20.8% 69 YOLOv7 83.7% 18.5% 28 YOLOX 80% 16.3% 30
Table 9. Results of ablation experiment
View tableView in ArticleTable 9. Results of ablation experiment
Algorithms mAP@0.50 mAP@0.50:0.95 FPS YOLOv5 89.6% 60.5% 67 YOLOv5+SD-CAN 93.2% 58.1% 60 YOLOv5+GMR 91.3% 60.4% 70 YOLOv5+SD-CAN+SIoU 93% 58.3% 60 YOLOv5+GMR+SIoU 92.3% 61.9% 70 YOLOv5 +SD-CAN+GMR 92.1% 57.9% 60 YOLOv5+SD-CAN+GMR+SIoU 95.9% 70.1% 59
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Wenxin Xia, Xiaogang Yang, Jianxiang Xi, Ruitao Lu, Xueli Xie. Structure characteristics sensing method of unmanned aerial vehicle group based on infrared detection[J]. Infrared and Laser Engineering, 2024, 53(1): 20230429
Paper Information
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Received: May. 28, 2023
Accepted: --
Published Online: Mar. 19, 2024
The Author Email: Xi Jianxiang (xijx07@mails.tsinghua.edu.cn)
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