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Updated: Jan 6, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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优化了具有可变形卷积的YOLOv8s框架,用于水下物体检测.

Xin Wang1, Ke Li2, Feiyan Fan3

  • 1College of Computer Science and Software Engineering, Hohai University, Nanjing, 211100, China.

Scientific reports
|November 27, 2025
PubMed
概括

本研究介绍了O-YOLOv8s-DC,这是一个优化的深度学习模型,用于水下物体检测. 它显著提高了在具有挑战性的水环境中检测小和封闭目标的性能.

关键词:
深度学习是一种深度学习.可变形卷积的可变形卷积.对象检测检测对象检测对象检测水下图像 水下图像 水下图像这是YOLOv8s.

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科学领域:

  • 计算机视觉 计算机视觉
  • 海洋技术 海洋技术
  • 人工智能的人工智能

背景情况:

  • 水下物体检测对于日益增长的水产经济至关重要.
  • 挑战包括小/封闭的目标,不同的物体形状,以及由于度导致的图像质量差.

研究的目的:

  • 开发一个优化的深度学习框架,O-YOLOv8s-DC,用于增强水下物体检测.
  • 解决现有模型在检测小,封闭和形态多样化的水下物体方面的局限性.

主要方法:

  • 拟议的O-YOLOv8s-DC框架集成可变形卷积 (C2f_DC),深度加权双向特征金字塔 (DeepBiFPN),内容意识特征重组 (CARAFE) 和高效的多尺度注意力 (EMA).
  • 进行了废弃研究,以验证单个模块的贡献.
  • 在LFIW和OI数据集上评估性能.

主要成果:

  • O-YOLOv8s-DC显著优于SSD,YOLOv8s和DETR等主流型号的性能.
  • 与原来的YOLOv8s相比,实现了更高的AP@[0.50:0.05:0.95]
  • 在严格的IoU值 (例如,AP@0.75) 上,对被封闭的目标的性能有所提高,小目标识别精度有所提高.

结论:

  • O-YOLOv8s-DC在复杂的环境中提供可靠的水下物体检测.
  • 为水生生态保护和可持续的水下作业提供技术支持.
  • 综合模块有效地解决了水下物体检测方面的挑战.