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一艘多尺度旋转船的目标是复杂场景中的遥感图像的检测网络

Siyu Li¹, Fei Yan^2,3, Yunqing Liu^1,4

¹School of Electronic Information and Engineering, Changchun University of Science and Technology, Changchun, China.

Scientific reports

|January 20, 2025

概括

这项研究介绍了MSRO-Net,这是一种新的深度学习模型,用于在复杂的遥感图像中检测船只. 该网络通过有效处理小目标和各种方向,显著提高了准确性.

科学领域:

计算机视觉计算机视觉
遥感遥感遥感遥感
人工智能的人工智能

背景情况:

在复杂的遥感图像中检测小,旋转的船舶目标是具有挑战性的,因为其功能有限和方向变化.
现有的对象检测算法在这种情况下经常与错过和错误检测作斗争.

研究的目的:

提出一个多尺度旋转检测网络 (MSRO-Net),用于在遥感图像中准确检测旋转的船舶目标.
增强网络从目标中提取具有有限信息和随机定向的特征的能力.

主要方法:

开发了一个CNN-Transformer混合架构,用于协作特征提取,集成了一个坐标感知金字塔特征聚合 (CAPP) 模块.
引入了一个采样特征重建金字塔 (ARFPN-C),利用自适应旋转卷积 (ARC) 提高感知能力和多尺度特征融合.

主要成果:

在HRSC2016和DOTA数据集上,MSRO-Net取得了卓越的表现.
证明了高准确性,mAP07在HRSC2016上为90.70%,mAP12在HRSC2016上为98.98%;在DOTA中检测船舶的mAP为89.46%.

结论:

拟议的MSRO-Net有效地解决了在遥感图像中检测旋转船舶目标的挑战.
网络的混合架构和新型模块显著提高了检测准确性和稳定性.

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