Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (7): 2200-2207.DOI: 10.11772/j.issn.1001-9081.2023071033
• Multimedia computing and computer simulation • Previous Articles Next Articles
Received:
2023-07-31
Revised:
2023-09-23
Accepted:
2023-10-10
Online:
2023-10-26
Published:
2024-07-10
Contact:
Zhangjian JI
About author:
DU Na, born in 1999, M. S. candidate. Her research interests include computer vision, object detection.Supported by:
通讯作者:
姬张建
作者简介:
杜娜(1999—),女,山西吕梁人,硕士研究生,主要研究方向:计算机视觉、目标检测。基金资助:
CLC Number:
Zhangjian JI, Na DU. Tiny target detection based on improved VariFocalNet[J]. Journal of Computer Applications, 2024, 44(7): 2200-2207.
姬张建, 杜娜. 基于改进VariFocalNet的微小目标检测[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2200-2207.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023071033
算法 | 主干 | mAP | ||||||
---|---|---|---|---|---|---|---|---|
文献[ | RLANet-50 | 11.1 | 25.8 | 8.1 | 2.6 | 12.0 | 14.9 | 21.6 |
VFNet[ | ResNet-50 | 10.2 | 23.5 | 7.2 | 2.3 | 11.0 | 13.3 | 19.8 |
Faster R-CNN[ | ResNet-50 | 11.1 | 26.3 | 7.6 | 0.0 | 7.2 | 23.3 | 33.6 |
Cascade R-CNN[ | ResNet-50 | 13.8 | 30.8 | 10.5 | 0.0 | 10.6 | 25.5 | 36.6 |
SSD[ | ResNet-50 | 7.0 | 21.7 | 2.8 | 1.0 | 4.7 | 11.5 | 13.5 |
RetinaNet[ | ResNet-50 | 8.7 | 22.3 | 4.8 | 2.4 | 8.9 | 12.2 | 16.0 |
RepPonits[ | ResNet-50 | 9.2 | 23.6 | 5.3 | 2.5 | 9.2 | 12.9 | 14.4 |
FCOS[ | ResNet-50 | 12.6 | 30.4 | 8.1 | 2.3 | 12.2 | 17.2 | 25.0 |
FoveaBox[ | ResNet-50 | 8.1 | 19.8 | 5.1 | 0.9 | 5.8 | 13.4 | 15.9 |
PAA[ | ResNet-50 | 10.0 | 26.5 | 6.7 | 3.5 | 10.5 | 13.1 | 22.1 |
ATSS[ | ResNet-50 | 12.8 | 30.6 | 8.5 | 1.9 | 11.6 | 19.5 | 29.2 |
OTA[ | ResNet-50 | 10.4 | 24.3 | 7.2 | 2.5 | 11.9 | 15.7 | 20.9 |
AutoAssign[ | ResNet-50 | 12.2 | 32.0 | 6.8 | 3.4 | 13.7 | 16.0 | 19.1 |
GFL[ | ResNet-50 | 11.1 | 25.1 | 8.2 | 2.3 | 11.9 | 14.6 | 23.0 |
TridentNet[ | ResNet-50 | 7.5 | 20.9 | 3.6 | 1.0 | 5.8 | 12.6 | 14.0 |
本文算法 | RLANet-50 | 14.9 | 36.5 | 9.8 | 4.4 | 16.3 | 18.8 | 22.9 |
Tab. 1 Comparison of detection results of different detection algorithms on AI-TOD dataset
算法 | 主干 | mAP | ||||||
---|---|---|---|---|---|---|---|---|
文献[ | RLANet-50 | 11.1 | 25.8 | 8.1 | 2.6 | 12.0 | 14.9 | 21.6 |
VFNet[ | ResNet-50 | 10.2 | 23.5 | 7.2 | 2.3 | 11.0 | 13.3 | 19.8 |
Faster R-CNN[ | ResNet-50 | 11.1 | 26.3 | 7.6 | 0.0 | 7.2 | 23.3 | 33.6 |
Cascade R-CNN[ | ResNet-50 | 13.8 | 30.8 | 10.5 | 0.0 | 10.6 | 25.5 | 36.6 |
SSD[ | ResNet-50 | 7.0 | 21.7 | 2.8 | 1.0 | 4.7 | 11.5 | 13.5 |
RetinaNet[ | ResNet-50 | 8.7 | 22.3 | 4.8 | 2.4 | 8.9 | 12.2 | 16.0 |
RepPonits[ | ResNet-50 | 9.2 | 23.6 | 5.3 | 2.5 | 9.2 | 12.9 | 14.4 |
FCOS[ | ResNet-50 | 12.6 | 30.4 | 8.1 | 2.3 | 12.2 | 17.2 | 25.0 |
FoveaBox[ | ResNet-50 | 8.1 | 19.8 | 5.1 | 0.9 | 5.8 | 13.4 | 15.9 |
PAA[ | ResNet-50 | 10.0 | 26.5 | 6.7 | 3.5 | 10.5 | 13.1 | 22.1 |
ATSS[ | ResNet-50 | 12.8 | 30.6 | 8.5 | 1.9 | 11.6 | 19.5 | 29.2 |
OTA[ | ResNet-50 | 10.4 | 24.3 | 7.2 | 2.5 | 11.9 | 15.7 | 20.9 |
AutoAssign[ | ResNet-50 | 12.2 | 32.0 | 6.8 | 3.4 | 13.7 | 16.0 | 19.1 |
GFL[ | ResNet-50 | 11.1 | 25.1 | 8.2 | 2.3 | 11.9 | 14.6 | 23.0 |
TridentNet[ | ResNet-50 | 7.5 | 20.9 | 3.6 | 1.0 | 5.8 | 12.6 | 14.0 |
本文算法 | RLANet-50 | 14.9 | 36.5 | 9.8 | 4.4 | 16.3 | 18.8 | 22.9 |
算法 | mAP | |||
---|---|---|---|---|
VFNet[ | 13.5 | 32.1 | 2.6 | 12.3 |
RetinaNet[ | 8.9 | 24.2 | 2.7 | 8.4 |
FCOS[ | 12.0 | 30.2 | 2.2 | 11.1 |
本文算法 | 15.4 | 37.4 | 4.4 | 15.8 |
Tab. 2 Comparison of detection results of different detection algorithms on AI-TODv2 dataset
算法 | mAP | |||
---|---|---|---|---|
VFNet[ | 13.5 | 32.1 | 2.6 | 12.3 |
RetinaNet[ | 8.9 | 24.2 | 2.7 | 8.4 |
FCOS[ | 12.0 | 30.2 | 2.2 | 11.1 |
本文算法 | 15.4 | 37.4 | 4.4 | 15.8 |
算法 | mAP | |||
---|---|---|---|---|
VFNet[ | 23.4 | 38.2 | 1.7 | 7.1 |
RetinaNet[ | 17.4 | 29.4 | 0.8 | 2.5 |
FCOS[ | 14.1 | 25.5 | 0.1 | 2.1 |
本文算法 | 25.0 | 44.4 | 2.8 | 10.3 |
Tab. 3 Comparison of detection results of different detection algorithms on VisDrone2019 dataset
算法 | mAP | |||
---|---|---|---|---|
VFNet[ | 23.4 | 38.2 | 1.7 | 7.1 |
RetinaNet[ | 17.4 | 29.4 | 0.8 | 2.5 |
FCOS[ | 14.1 | 25.5 | 0.1 | 2.1 |
本文算法 | 25.0 | 44.4 | 2.8 | 10.3 |
主干 | FEM | 标签分配 | Wasserstein损失 | mAP | ||||||
---|---|---|---|---|---|---|---|---|---|---|
ResNet-50 | 10.2 | 23.5 | 7.2 | 2.3 | 11.0 | 13.3 | 19.8 | |||
√ | 11.2 | 26.3 | 7.7 | 2.5 | 12.2 | 14.6 | 21.1 | |||
√ | 12.7 | 29.5 | 9.2 | 4.1 | 14.3 | 14.7 | 19.5 | |||
√ | 10.7 | 25.8 | 7.0 | 2.4 | 11.5 | 14.8 | 21.2 | |||
√ | √ | √ | 13.7 | 33.2 | 9.0 | 4.0 | 15.2 | 16.8 | 21.7 | |
RLANet-50 | 12.3 | 29.3 | 8.2 | 2.8 | 13.1 | 16.8 | 23.4 | |||
√ | 11.9 | 28.5 | 8.2 | 2.7 | 12.9 | 16.1 | 22.7 | |||
√ | 14.1 | 33.8 | 9.7 | 5.1 | 15.6 | 17.9 | 22.1 | |||
√ | 11.2 | 27.3 | 7.3 | 2.5 | 11.7 | 15.9 | 24.3 | |||
√ | √ | √ | 14.9 | 36.5 | 9.8 | 4.4 | 16.3 | 18.8 | 22.9 |
Tab. 4 Ablation experiment results
主干 | FEM | 标签分配 | Wasserstein损失 | mAP | ||||||
---|---|---|---|---|---|---|---|---|---|---|
ResNet-50 | 10.2 | 23.5 | 7.2 | 2.3 | 11.0 | 13.3 | 19.8 | |||
√ | 11.2 | 26.3 | 7.7 | 2.5 | 12.2 | 14.6 | 21.1 | |||
√ | 12.7 | 29.5 | 9.2 | 4.1 | 14.3 | 14.7 | 19.5 | |||
√ | 10.7 | 25.8 | 7.0 | 2.4 | 11.5 | 14.8 | 21.2 | |||
√ | √ | √ | 13.7 | 33.2 | 9.0 | 4.0 | 15.2 | 16.8 | 21.7 | |
RLANet-50 | 12.3 | 29.3 | 8.2 | 2.8 | 13.1 | 16.8 | 23.4 | |||
√ | 11.9 | 28.5 | 8.2 | 2.7 | 12.9 | 16.1 | 22.7 | |||
√ | 14.1 | 33.8 | 9.7 | 5.1 | 15.6 | 17.9 | 22.1 | |||
√ | 11.2 | 27.3 | 7.3 | 2.5 | 11.7 | 15.9 | 24.3 | |||
√ | √ | √ | 14.9 | 36.5 | 9.8 | 4.4 | 16.3 | 18.8 | 22.9 |
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