复杂场景SAR飞机识别,结合注意力机制和内部卷积操作员
Wansi Liu1,2, Huan Wang3, Jiapeng Duan1,2
1School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
一个新的YOLOv7-MTI模型通过整合注意力机制和卷积来增强合成孔径雷达 (SAR) 飞机检测. 这种方法有效地减少了背景干扰,提高了各种飞机类型的实时识别精度.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 合成孔径雷达 (SAR) 为飞机监控提供全天候,全时观测能力.
- 来自机场基础设施的复杂背景散射对SAR飞机检测提出了重大挑战.
- 实时处理对于基于SAR的有效飞机识别系统至关重要.
研究的目的:
- 提出一个增强的YOLOv7-MTI模型,以改善SAR图像中的飞机检测.
- 为应对复杂的背景干扰和实时处理的需求所面临的挑战.
- 为了利用注意力机制和内向来提高识别表现.
主要方法:
- 集成多TASP-Conv网络 (MTCN) 模块,用于提取低层次的语义和空间信息.
- 整合卷曲以适应调整重量,加强飞机散射点和抑制背景噪音.
- 开发YOLOv7-MTI模型,结合MTCN和卷积来实现增强的特征表示和降低噪音.
主要成果:
- 在SAR-AIRcraft-1.0数据集上,YOLOv7-MTI模型实现了93.51%的平均平均精度 (mAP) 和96.45%的平均回忆 (mRecall).
- 性能超越了包括Faster R-CNN,SSD,YOLOv5,YOLOv7和YOLOv8.8在内的已有的模型.
- 与基本的YOLOv7相比,YOLOv7-MTI显示了mAP (+1.47%),mRecall (+1.64%) 和每秒 (FPS) (+8.27%) 的改进.
结论:
- 拟议的YOLOv7-MTI模型有效地减轻了用于飞机识别的SAR图像的复杂背景干扰.
- 该模型显示了检测准确度和处理速度之间的卓越平衡.
- 这项研究提供了有价值的见解和基于SAR的飞机检测的强大框架.
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