收稿日期:
2024-01-02
修回日期:
2024-01-19
接受日期:
2024-03-15
出版日期:
2024-04-02
发布日期:
2024-03-29
通讯作者:
刘贞报
E-mail:liuzhenbao@nwpu.edu.cn
基金资助:
Xiao WANG1, Zhenbao LIU1(), Zhongke SHI2
Received:
2024-01-02
Revised:
2024-01-19
Accepted:
2024-03-15
Online:
2024-04-02
Published:
2024-03-29
Contact:
Zhenbao LIU
E-mail:liuzhenbao@nwpu.edu.cn
Supported by:
摘要:
无人机在检测和跟踪目标过程中受到目标伪装、目标遮挡、移动躲避以及假目标等因素的干扰,而无人机目标附近的阴影区域加剧了这些因素对目标检测和跟踪性能的影响,因此检测无人机目标阴影区域是无人机领域的重要研究任务之一。现有无人机目标阴影检测方法面临训练数据数量有限、数据收集标注困难以及无人机目标中存在大量尺寸较小的细碎阴影区域等问题,针对这些问题,提出一种基于残差混合监督网络的无人机目标阴影检测算法。首先针对无人机目标阴影检测任务的特点设计分辨率注意力网络,在结合底层纹理特征和高层语义特征的过程中,更准确地保留底层纹理特征。然后设计混合监督网络扩充训练数据集,结合普通阴影检测数据集和无人机目标阴影检测数据集训练教师网络,使用无人机阴影检测数据集和教师网络的参数训练学生网络。同时设计残差图像,利用教师网络检测结果和标准结果之间的残差图像扩充训练数据集,使阴影检测网络更加关注细碎阴影区域。最后,在2个公开实验数据集上和已有方法进行对比实验,在各个评价参数上取得了最多41.6%的提升效果,证明所提无人机目标阴影检测算法较好的解决了现有方法存在的问题,具有较高的准确性。
中图分类号:
王潇, 刘贞报, 史忠科. 基于残差混合监督网络的无人机目标阴影检测[J]. 航空学报, 2024, 45(17): 530062-530062.
Xiao WANG, Zhenbao LIU, Zhongke SHI. Shadow detection of UAV target based on residual mixed supervision network[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(17): 530062-530062.
表 1
本文方法和对比方法的评价参数
方法 | AISD数据集 | SSAD数据集 | |||||||
---|---|---|---|---|---|---|---|---|---|
F1 | OA | IoU | BER | F1 | OA | IoU | BER | ||
DeepLabV3 | 91.15 | 95.46 | 83.74 | 5.66 | 81.31 | 92.13 | 70.96 | 16.70 | |
MTMT | 90.68 | 94.46 | 78.26 | 8.48 | 79.02 | 88.47 | 60.71 | 18.96 | |
DSSDNet | 91.79 | 95.57 | 6.24 | 79.31 | |||||
GSCA-UNet | 91.69 | 96.29 | 84.88 | 5.51 | |||||
CADNet | 91.21 | ||||||||
ESPFNet | 92.04 | ||||||||
FSDNet | 92.36 | 95.81 | 85.51 | 5.53 | 82.65 | 92.25 | 73.31 | 15.34 | |
DLA-PSO | 82.70 | 85.26 | |||||||
CDANet | 92.41 | 83.96 | |||||||
MRPFANet | 92.61 | 96.11 | 86.24 | 5.42 | 85.21 | 93.38 | 75.84 | 14.99 | |
本文方法 | 93.28 | 96.13 | 85.61 | 3.88 | 86.55 | 93.89 | 74.29 | 8.75 |
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