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YOLO-AR:基于YOLOv11的改进的人造珊瑚礁细分算法

Yuxiang Wu1, Tingchen Jiang1, Zhi Xi2

  • 1College of Marine Technology and Surveying, Jiangsu Ocean University, Lianyungang 222005, China.

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概括
此摘要是机器生成的。

这项研究介绍了YOLO-AR,这是一种改进的AI模型,用于在声纳图像中准确检测人工珊瑚礁. 该模型增强了海洋资源管理和生态保护工作.

关键词:
这就是YOLOv11的意义.人工珊瑚礁探测器 人工珊瑚礁探测器深度学习是一种深度学习.多束声纳图像的多束声纳图像

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科学领域:

  • 海洋生态海洋生态学
  • 人工智能的人工智能是人工智能.
  • 声纳成像 声纳成像

背景情况:

  • 人造珊瑚礁对于海洋息地恢复和渔业资源管理至关重要.
  • 精确识别人造珊瑚礁对于生态保护至关重要.
  • 多束声纳图像对于绘制海底结构的地图至关重要.

研究的目的:

  • 为多束声纳图像开发精确的人造珊瑚礁细分模型.
  • 提高在海洋环境中人工珊瑚礁检测的效率和准确性.
  • 为海洋应用创建一个轻量级但高性能的深度学习模型.

主要方法:

  • 提出了一个改进的基于YOLOv11的模型,命名为YOLO-AR.
  • 集成的动态卷积坐标注意力 (DCCA),以提高复杂的海底环境中的稳定性.
  • 集成了一个小物体检测层与动态采样 (DySample) 和高级下方采样 (ADown) 进行了集成,以改善细分和降低复杂性.

主要成果:

  • YOLO-AR实现了0.912的平均平均精度 (mAP@0.5),0.832的十字路口 (IOU) 和0.908.908的F1得分.
  • 这款车型拥有轻量化结构,有267万个参数,尺寸为5.58MB.
  • 在现实世界声纳图像中,与其他先进模型相比,展示了优越的细分性能.

结论:

  • 在多束声纳图像中,YOLO-AR精确地对人工珊瑚礁进行细分.
  • 该模型的轻量级设计和高性能使其在实际的海洋应用中非常有效.
  • 这项技术支持海洋养殖发展和有效的渔业资源管理.