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同时,基于视觉的鱼类实例细分,物种分类和尺寸回归.

Pau Climent-Perez1, Alejandro Galán-Cuenca1, Nahuel E Garcia-d'Urso1

  • 1Department of Computer Technology, University of Alicante, San Vicente del Raspeig, Spain.

PeerJ. Computer science
|March 4, 2024
PubMed
概括
此摘要是机器生成的。

根据市场图像进行鱼类自动识别和大小估计,有助于渔业管理. 这项技术提高了可持续渔业实践和生物多样性保护的数据准确性.

关键词:
计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.鱼类大小估计 鱼类大小估计分段化 分段化 分段化 分段化种类识别 种类识别

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科学领域:

  • 海洋生物学 海洋生物学
  • 计算机视觉 计算机视觉
  • 渔业 科学 渔业 科学

背景情况:

  • 过度捕捞导致生物多样性丧失,并影响人类社区.
  • 目前的渔业数据收集依赖于小样本,导致不准确的评估.
  • 数字化为改善渔业数据采集提供了机会.

研究的目的:

  • 开发一个自动化工作流程,从鱼市图像中提取有价值的渔业数据.
  • 为了实现准确的鱼类实例细分,物种分类和大小估计.
  • 支持渔业保护和可持续开发的决策.

主要方法:

  • 利用计算机视觉工作流进行鱼图像分析.
  • 实现实例细分和物种分类算法.
  • 开发了一种从未校准图像中估计鱼类大小的方法.

主要成果:

  • 在鱼类实例细分和物种分类 (在50%的IOU) 中,平均平均精度 (mAP) 为70.42%.
  • 获得了鱼类大小估计的1.27厘米的平均平均误差 (MAE).
  • 在鱼类批发市场展示了自动化,细粒度数据提取的潜力.

结论:

  • 鱼类市场的自动图像分析可以显著提高渔业数据收集.
  • 开发的工作流提供了关于鱼类,数量和大小的准确信息.
  • 这项技术支持为可持续的渔业管理和保护工作做出更明智的决策.