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一个水下图像数据集用于遮蔽感知鱼类实例细分.

Xiaoyang Wang1,2,3,4, Huihui Yu1,2,3,5, Chi Zhang1,2,3,4

  • 1National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China.

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一个新的数据集,即鱼类封闭数据集 (FOD),解决了用于水产养殖的鱼类实例细分方面的挑战. 它具有多样化的,注释图像,以提高模型性能,特别是与封闭的鱼.

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科学领域:

  • 水产养殖技术 水产养殖技术
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 精确的水下鱼类感知对于智能水产养殖至关重要.
  • 图像中的鱼类遮蔽阻碍了精确的实例细分.
  • 强大的模型需要具有详细注释的大型数据集.

研究的目的:

  • 介绍鱼类封闭数据集 (FOD),用于封闭意识的鱼类实例细分.
  • 为评估不同封闭级别下的实例细分模型提供一个基准.
  • 促进鱼类自动化监测和管理方面的进步.

主要方法:

  • 从精密水产养殖基地收集了14376张水下图像.
  • 标注了144,894个鱼类实例,在三个封闭级别 (整体,部分,片段) 中提供像素级细节.
  • 包括原始和合成图像,用于全面的模型培训和评估.

主要成果:

  • 鱼类阻塞数据集 (FOD) 能够在阻塞场景中进行细粒度的性能评估.
  • 八个实例细分模型进行了基准测试,评估了基于检测和没有建议的架构.
  • Mask2Former表现出优越的整体细分性能,特别是在高度封闭的条件下.

结论:

  • 在开发和评估鱼类实例细分模型时,FOD是一个宝贵的资源.
  • 解决阻塞是提高智能水产养殖中鱼类感知能力的关键.
  • 像Mask2Former这样的先进模型在水产养殖中显示出对现实世界应用的希望.