Opto-Electronic Engineering, Volume. 51, Issue 9, 240149-1(2024)
Adaptive foreground focusing for target detection in UAV aerial images
Figures & Tables(13)
Fig. 3. Adaptive two-dimensional feature sampling (ATFS) structure
Fig. 4. Structure of multi-path full-text information integration (MPFT)
Fig. 7. Adaptive foreground focus detect head (AFF_Detect) structure
Fig. 8. Comparison of evaluation indicators between the YOLOv8s and improved mode
Fig. 9. Comparison test visualisation results. (a) RetinaNet; (b) YOLOv5s;(c) Faster-RCNN; (d) TPH-YOLOv5; (e) YOLOv7-tiny; (f) YOLOv8s;(g) YOLOv10s; (h) Improved YOLOv5; (i) Deformmable-DETR; (j) Ours
Table 1. Ablation experiments of the proposed algorithm in the VisDrone2019 dataset
View tableView in ArticleTable 1. Ablation experiments of the proposed algorithm in the VisDrone2019 dataset
Number YOLOv8s PFRC ATFS MPFT AFFDH Precision/% Recall/% mAP@0.5/% GFLOPs 1 √ × × × × 49.7 37.5 38.5 28.8 2 √ √ × × × 52.7 41.7 40.9 34.3 3 √ × √ × × 53.4 42.8 41.8 27.1 4 √ × × √ × 54.5 41.5 42.7 25.5 5 √ × × × √ 52.4 41.4 39.8 26.8 6 √ √ √ × × 54.0 43.4 43.2 32.0 7 √ √ √ √ × 55.8 44.2 44.3 29.2 8 √ √ √ √ √ 58.1 44.6 45.1 28.9
Table 2. Comparison results of the accuracy of ablation experiments by category/%
View tableView in ArticleTable 2. Comparison results of the accuracy of ablation experiments by category/%
Number Pedestrian People Bicycle Car Van Truck Tricycle Awning-tricycle Bus Motor mAP@0.5 1 37.2 27.6 14.7 77.4 42.9 39.0 23.8 21.5 56.1 39.4 38.5 2 38.2 22.3 18.9 81.0 44.1 41.2 18.7 25.5 58.1 35.6 40.9 3 39.0 31.0 18.4 80.9 44.9 42.9 24.6 19.9 60.3 39.7 41.8 4 41.8 33.2 17.6 83.8 45.4 40.6 25.8 26.1 61.5 45.6 42.7 5 33.3 22.1 16.3 73.4 39.4 41.0 21.1 22.9 57.4 40.9 39.8 6 44.8 34.7 18.9 84.7 46.6 50.7 26.8 25.4 58.3 49.1 43.2 7 52.8 42.0 20.1 83.3 46.7 43.8 28.0 26.7 60.8 52.7 44.3 8 53.9 41.3 24.1 87.8 50.5 45.3 31.6 28.9 62.6 55.2 45.1
Table 3. Results of comparison experiments on VisDrone2019 dataset
View tableView in ArticleTable 3. Results of comparison experiments on VisDrone2019 dataset
Model Precision/% Recall/% mAP@0.5/% mAP@0.5:0.95/% GFLOPs FPS YOLOv5s 48.7 35.0 34.4 18.5 15.7 120 RetinaNet 35.5 21.9 20.3 12.5 92.9 26 TPH-YOLOv5 49.4 37.0 36.9 19.1 14.6 125 YOLOv7-tiny 50.1 41.2 37.6 22.6 12.9 113 Deformmable-DETR 52.4 45.0 44.2 25.7 179.3 69 YOLOv8s 49.7 37.5 38.5 22.1 28.8 129 YOLOv10s 55.4 40.7 41.1 23.8 21.6 133 Improved YOLOv5 57.7 43.0 43.9 24.9 34.3 99 Faster-RCNN 48.0 35.1 35.0 21.8 42.5 23 Ours 58.1 44.6 45.1 28.3 28.9 145
Table 4. Results of comparison experiments on VisDrone2021 dataset
View tableView in ArticleTable 4. Results of comparison experiments on VisDrone2021 dataset
Model Precision/% Recall/% mAP@0.5/% mAP@0.5:0.95/% GFLOPs FPS YOLOv5s 46.6 33.1 31.9 16.1 15.7 115 RetinaNet 31.5 18.9 15.3 10.5 92.9 21 TPH-YOLOv5 45.4 34.0 33.6 16.3 14.6 119 YOLOv7-tiny 48.8 39.2 34.6 19.9 12.9 105 Deformmable-DETR 51.5 42.1 42.0 22.4 179.3 62 YOLOv8s 47.7 37.5 37.4 19.8 28.8 120 YOLOv10s 50.7 38.1 40.5 21.2 21.6 127 Improved YOLOv5s 52.2 40.7 42.1 22.6 34.3 88 Faster-RCNN 46.1 33.6 32.3 18.8 42.5 20 Ours 53.1 42.6 43.1 24.3 28.9 138
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Zhenjiu Xiao, Zhengwei Wu, Jiehao Zhang, Haicheng Qu. Adaptive foreground focusing for target detection in UAV aerial images[J]. Opto-Electronic Engineering, 2024, 51(9): 240149-1
Paper Information
Category: Article
Received: Jun. 28, 2024
Accepted: Aug. 6, 2024
Published Online: Dec. 12, 2024
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