#DeOlhoNosCorais:一个多边形注释数据集,以优化珊瑚监测
Daniel P Furtado1, Edson A Vieira2, Wildna Fernandes Nascimento2
1Department of Informatics, Federal University of Paraná, Curitiba, PR, Brazil.
PeerJ
|November 13, 2023
概括
公民科学家可以通过在社交媒体上发布照片来帮助监测珊瑚礁. 机器学习模型有效地分析这些图像用于珊瑚物种识别和健康评估,帮助保护工作.
科学领域:
- 海洋生物学 海洋生物学
- 人工智能的人工智能
- 保护科学 保护科学
背景情况:
- 珊瑚礁是重要的海洋生态系统,为各种物种提供息地.
- 气候变化导致热应激,导致珊瑚漂白和共生藻类的丧失.
- 传统的珊瑚礁监测是昂贵和耗时的,限制了大规模的评估.
研究的目的:
- 开发和评估用于分析通过社交媒体收集的珊瑚礁图像的机器学习模型.
- 通过利用公民科学贡献和人工智能支持的图像分析来优化珊瑚监测.
- 评估用于珊瑚礁监测的图像分类和语义细分模型的性能.
主要方法:
- 利用社交媒体 (Instagram) 来收集公民科学数据,使用标签#DeOlhoNosCorais.
- 应用机器学习模型,包括EfficientNet和物流回归,用于图像分类.
- 使用U-Net Pix2Pix模型对珊瑚礁图像进行语义细分.
- 使用本地可解释模型不可知解释 (LIME) 算法来分析模型性能.
主要成果:
- 效率网和物流回归的组合产生了最好的分类结果.
- U-Net Pix2Pix模型在语义细分方面实现了86%的像素级准确性.
- 开发的工具显示了增强图像选择和简化分析的潜力.
结论:
- 机器学习模型可以有效地分析公民科学生成的珊瑚礁图像.
- 这种方法为大规模珊瑚礁监测提供了一种成本效益高效的方法.
- 这些发现支持开发用于珊瑚礁保护和研究的先进工具.
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