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一种基于改进的YOLOv8nn识别死海的方法

Lizhen Zhang¹, Chong Xu¹, Sai Jiang¹

¹College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.

Sensors (Basel, Switzerland)

|July 30, 2025

概括

迅速检测死海对于水产养殖至关重要. 本研究介绍了YOLOv8n-Deadfish,这是一种轻量级模型,可以提高死鱼检测的准确性和速度,同时降低计算成本.

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计算机视觉
机器学习是机器学习.

背景情况:

水产养殖中的死鱼导致污染和疾病传播.
现有的死鱼物体检测模型是复杂和不准确的,特别是与阻塞.

研究的目的:

开发一种轻量级,高精度的物体检测模型,用于识别死海.
提高智能水产养殖系统中死鱼检测的效率和准确性.

主要方法:

开发了YOLOv8n-Deadfish,包含了一个扩展的数据集和一个新的C2f-faster-EMA模块.
集成了一个加权的双向特征金字塔网络 (BiFPN),用于增强特征融合.
利用Inner-CIoU损失函数来改善边界框回归和模型融合.

主要成果:

YOLOv8n-Deadfish实现了90.0%的准确性,90.4%的回忆力和93.6%的平均精度.
模型参数和GFLOP分别减少了23.3%和18.5%.
检测速度增加到424.6 FPS,与基础YOLOv8n.n.相比显著改善.

结论:

YOLOv8n-Deadfish模型为在水产养殖中检测死海提供了一种优越的解决方案.
这项技术为更有效和高效的智能水产养殖管理提供了基础.
该模型的轻量级设计和高性能解决了以前检测方法的局限性.

关键词:

这就是YOLOv8的意义.死亡海鱼是什么意思深度学习是一种深度学习.目标检测目标检测目标检测

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FISH - Fluorescent In-situ Hybridization

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