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Updated: Sep 10, 2025

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Swimming Performance Assessment in Fishes
Published on: May 20, 2011
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计算机视觉检测了大西洋养殖鱼 (Salmo salar) 的毛细分数和通风率之间的联系
Quynh Le Khanh Vo1,2, Kylie A Pitt1, Colin Johnston3
1School of Environment and Science, Australian Rivers Institute, Griffith University, Gold Coast, Queensland, Australia.
Journal of fish diseases
|August 26, 2025
概括
通过将鱼的通风率与状分数联系起来,计算机视觉可以非侵入性地监测大西洋鱼的状状况. 这种方法有助于识别水产养殖中改善鱼类福利的潜在健康问题.
科学领域:
- 水产养殖
- 动物健康
- 计算机视觉
背景情况:
- 养殖的大西洋鱼的状况不佳会影响它们的福利,并导致呼吸困难.
- 目前的状况监测依赖于手动的"评分", 这对鱼来说是劳动密集和压力.
- 病,水母刺痛和有毒藻类等因素导致健康状况不佳.
研究的目的:
- 测试一种非侵入性的计算机视觉方法来评估大西洋鱼状况.
- 调查商业鱼养殖场的毛分数与鱼的通风率之间的关系.
- 确定计算机视觉是否可以帮助识别养殖鱼的健康和福利问题.
主要方法:
- 计算机视觉模型使用卷积神经网络检测到鱼头和分类口腔状态 (开放/关闭).
- 通过追踪检测方法根据口腔运动频率估计了通风速率.
- 分析了来自240个视频的通风率,以及总结分数和环境数据 (温度,溶解氧气,鱼的体重).
主要成果:
- 多重线性回归显示了通风率和总点之间的正相关性.
- 计算机视觉方法成功估计了鱼的通风率.
- 虽然AGD没有得到证实,但牙得分表明了牙的一般健康状况.
结论:
- 计算机视觉方法显示了非侵入性监测鱼状况的潜力.
- 这种方法可以帮助水产养殖管理人员识别需要进一步健康评估的鱼类.
- 这项技术有助于加强鱼养殖中的鱼类福利监督和管理实践.
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