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  • 1College of Power Engineering, Naval University of Engineering, Wuhan 430033, China.

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概括

本研究介绍了一种高效的YOLOv11网络模块,用于远程传感图像中的高精度,任意定向的船舶检测. 该方法提高了准确性和速度,超过了现有的最先进的技术.

关键词:
这是GSConv模块.任意定向的船舶检测系统跨阶段的部分阶段.改进了YOLO1111的功能.轻量级网络是轻量级的网络.多尺度扩展的注意力

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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:

  • 在遥感图像中任意定向的船舶检测是具有挑战性的,因为高分辨率,清晰度差,尺度差异.
  • 现有的方法往往难以平衡高精度和检测速度.

研究的目的:

  • 设计一个轻量级和高效的模块,用于在遥感图像中高精度,任意定向的船舶检测.
  • 提高YOLOv11网络的性能,以完成这一具体任务.

主要方法:

  • 开发了一个轻量级和高效的多尺度功能扩展部模块,集成到YOLOv11网络中.
  • 利用多尺度扩展的注意力来捕捉语义细节.
  • 采用跨阶段的部分网络进行空间语义信息交互.
  • 引入了GSConv模块,用于在特征传输过程中保存语义信息.

主要成果:

  • 拟议的方法显示了轻量结构和高精度.
  • 与最先进的方法相比,实现了优越的船舶检测性能.
  • 在HRSC2016数据集中,与YOLOv11n.95相比,观察到3.1%的mAP@0.5和3.3%的mAP@0.5:0.95的改善.
  • 在MMShip数据集上,与YOLOv11n.95相比,实现了1.9%的mAP@0.5和1.3%的mAP@0.5:0.95的改进.

结论:

  • 设计的模块有效地提高了远程传感图像中的任意定向船舶检测.
  • 该方法为准确和高效的船舶检测提供了一个有希望的解决方案,其性能优于现有的方法.