人工智能目前是否能够识别野生? 人类注释者与人工智能模型的比较,ODYSSEEEE
Brendan Campbell1, Alan Williams2, Kleio Baxevani1,3
1University of Delaware, College of Earth Ocean & Environment, School of Marine Science & Policy, Lewes, DE, United States.
Frontiers in robotics and AI
|June 23, 2025
概括
一个新的深度学习模型,ODYSSEE,可以快速识别活,但需要进一步开发. 目前的精度低于人类专家,图像质量影响性能.
科学领域:
- 海洋生物学 海洋生物学
- 生态监测 生态监测
- 计算生态学计算生态学
背景情况:
- 珊瑚礁是重要的生态系统,需要对人口进行监测.
- 传统的监控方法具有破坏性和劳动密集性.
- 对于敏感的环境,需要使用非破坏性方法.
研究的目的:
- 评估ODYSSEE深度学习模型用于活的识别.
- 将模型的准确性和速度与人类注释者进行比较.
- 识别影响模型和人类表现的因素.
主要方法:
- 在礁图像上使用了ODYSSEE深度学习模型.
- 将ODYSSEE的活鉴定与专家和非专家注释者进行了比较.
- 分析了预测错误和图像质量的影响.
主要成果:
- 比人类 (2.34-4.50h) 处理数据的速度明显快 (39.6s).
- 该模型的精度低于专家 (74%) 和非专家 (75%) 的精度 (63%).
- 更高的图像质量提高了人类的准确性,但降低了模型的准确性.
结论:
- ODYSSEE显示了快速丰度评估的潜力,但需要改进.
- 未来的工作应该专注于更高质量的培训数据和改进的算法.
- 区分活和死仍然是自动化系统面临的关键挑战.
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