一个基于深度学习的模型,用于从拖网内摄像头自动识别介质生物
Taraneh Westergerling1,2, Vaneeda Allken1, Webjørn Melle1
1Institute of Marine Research, Bergen, Norway.
PloS one
|January 21, 2026
概括
深度学习模型现在可以从拖网摄像头图像中识别半层生物,改善碳运输和食物网研究. 这种方法增强了对各种海洋物种的声学数据验证.
科学领域:
- 海洋生物学 海洋生物学
- 深海生态 深海生态
- 人工智能在渔业中的应用
背景情况:
- 中层生物对海洋碳运输和食物网至关重要.
- 声学方法检测到半层聚合物,但在物种识别方面存在困难.
- 拖网捕获用于验证,但具有诸如物种逃逸等限制.
研究的目的:
- 开发和评估一种深度学习模型,用于从拖网内图像中识别半类物种.
- 在不同的照明条件下 (白色和红色光) 评估模型的性能.
- 改进对中性种群的声学数据的验证.
主要方法:
- 在拖网内图像上训练了一种深度学习对象检测模型 (YOLOv11s).
- 在白色和红色光线下采集的图像使用了不同的增强设置.
- 确定了七个半类鱼群和更大的类鱼.
主要成果:
- 该模型在白光下实现了高精度 (加权平均精度~0.95).
- 红光也允许有效的检测 (加权平均值平均精度~0.77).
- 灯鱼,银色光鱼和巴拉库迪纳鱼的检测精度很高 (>0.89).
结论:
- 深度学习可以从拖网内摄像头数据中准确识别半类物种.
- 这种方法促进了快速,深度分层的数据收集和分析.
- 它为研究脆弱物种提供了有价值的工具,这些物种经常在拖网中丢失.
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