用YOLOv8自动识别西印度洋珊瑚礁鱼类家族，提升监测效率。
Automating Coral Reef Fish Family Identification on Video Transects Using a YOLOv8-Based Deep Learning Pipeline
- 基于YOLOv8构建深度学习流水线，自动化识别鱼种家族
- 最佳模型[email protected]达0.52，常见鱼类识别准确率高
- 首次为西印度洋提供区域专属鱼类自动识别基准
西印度洋珊瑚礁监测受限于水下视觉普查的劳动强度。本研究评估了一种基于YOLOv8的深度学习流水线，用于从肯尼亚和坦桑尼亚采集的视频断面中自动识别鱼类家族。针对24个鱼类家族构建了精选数据集，并在不同配置下进行测试，首次为西印度洋的自动化珊瑚礁鱼类监测提供了区域专属基准。最佳模型在[email protected]上达到0.52，对常见鱼类家族识别准确，但对稀有或复杂类群检测能力较弱。结果表明，深度学习可作为传统监测方法的可扩展补充。
原文摘要 · Abstract (English)
Coral reef monitoring in the Western Indian Ocean is limited by the labor demands of underwater visual censuses. This work evaluates a YOLOv8-based deep learning pipeline for automating family-level fish identification from video transects collected in Kenya and Tanzania. A curated dataset of 24 families was tested under different configurations, providing the first region-specific benchmark for automated reef fish monitoring in the Western Indian Ocean. The best model achieved [email protected] of 0.52, with high accuracy for abundant families but weaker detection of rare or complex taxa. Results demonstrate the potential of deep learning as a scalable complement to traditional monitoring methods.
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