Opto-Electronic Engineering, Volume. 52, Issue 5, 240287(2025)
Multi-level refined UAV image target detection
Figures & Tables(16)
Fig. 1. Schematic illustration of typical challenges in UAV image detection. (a) Complex background; (b) Sudden change in illumination;(c) Target occlusion; (d) Inconsistent scales
Fig. 8. Comparison of evaluation metrics between YOLO11n and the improved model
Fig. 9. Comparison of visualization effects on dataset VisDrone2019
Fig. 10. Comparison of visualization effects on dataset VisDrone2021
Table 1. Comparison of values for different convolution kernel combinations
View tableView in ArticleTable 1. Comparison of values for different convolution kernel combinations
Convolution kernel combination mAP@0.5/% mAP@0.5∶0.95/% Parameters/106 GFLOPs 2×2, 4×4, 6×6 41.5 22.0 2.8 3.9 3×3, 5×5, 7×7 45.2 26.5 3.5 5.8 3×3, 5×5, 8×8 46.5 27.8 4.7 6.5
Table 2. Experimental environment
View tableView in ArticleTable 2. Experimental environment
Environment Model Operating system Ubuntu 18.04 CPU Xeon(R) Gold 6430 GPU RTX 2080 Ti (11 GB) Programming language Python 3.10.0 CUDA 12.4
Table 3. Training parameters
View tableView in ArticleTable 3. Training parameters
Parameter Setting Training epochs 200 Batch size 64 Input image size 640×640 Initial learning rate 0.01
Table 4. Ablation experiment results of the proposed algorithm on the VisDrone2019 dataset
View tableView in ArticleTable 4. Ablation experiment results of the proposed algorithm on the VisDrone2019 dataset
Model Precision/% Recall/% mAP@0.5/% mAP@0.5∶0.95/% Parameters/106 GFLOPs YOLO11n 51.5 38.3 41.8 24.1 2.5 4.3 + CSP-SMSFF 52.7 40.2 45.2 26.5 3.5 5.8 + AFGCAttention 55.2 38.9 45.8 27.1 2.6 5.4 + SGCE-Head 51.3 37.5 42.3 25.4 2.3 4.5 + IPIoUv2 51.4 38.6 41.9 25.0 2.5 4.3 + CSP-SMSFF + AFGCAttention 56.2 41.6 46.8 28.2 3.6 7.0 + CSP-SMSFF + AFGCAttention + SGCE-Head 56.7 42.8 47.4 29.7 3.3 7.3 + CSP-SMSFF + AFGCAttention + SGCE-Head + IPIoUv2 56.8 43.5 47.5 30.0 3.3 7.3
Table 5. Comparison of different models on the VisDrone2019 dataset
View tableView in ArticleTable 5. Comparison of different models on the VisDrone2019 dataset
Model Precision/% Recall/% mAP@0.5/% mAP@0.5∶0.95/% FPS RetinaNet 35.5 21.9 20.3 12.5 26 Faster R-CNN 48.0 35.1 35.0 21.9 23 Deformmable-DETR 52.4 45.0 44.4 28.3 69 BDAD-YOLO 45.6 35.7 36.1 20.7 122 YOLOv10n 47.7 36.0 37.0 21.2 146 SSG-YOLOv7 48.6 37.1 42.6 24.6 98 YOLOv8 50.5 38.5 45.4 27.4 63 Ours 56.8 43.5 47.5 30.0 145
Table 6. Comparison of different models on VisDrone2021 dataset
View tableView in ArticleTable 6. Comparison of different models on VisDrone2021 dataset
Model Precision/% Recall/% mAP@0.5/% mAP@0.5∶0.95/% FPS RetinaNet 31.5 18.9 15.3 10.5 21 Faster R-CNN 46.1 33.6 32.3 18.8 20 Deformable-DETR 51.5 42.1 42.0 22.4 62 BDAD-YOLO 43.2 33.6 34.3 17.8 112 YOLOv10n 45.4 36.1 36.4 18.7 140 SSG-YOLOv7 47.9 37.6 40.7 22.2 96 YOLOv8 49.4 40.1 43.6 24.7 60 Ours 53.2 41.8 45.3 26.4 140
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Zhenjiu Xiao, Siyu Lai, Haicheng Qu. Multi-level refined UAV image target detection[J]. Opto-Electronic Engineering, 2025, 52(5): 240287
Paper Information
Category: Article
Received: Dec. 5, 2024
Accepted: Mar. 17, 2025
Published Online: Jul. 18, 2025
The Author Email: Siyu Lai (赖思宇)
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