用多环境水下图像训练模型，自动识别珊瑚白化状态
Deep Learning Models for Coral Bleaching Classification in Multi-Condition Underwater Image Datasets
- 基于全球多环境水下数据集，对比ResNet/ViT/CNN三类模型
- 优化后CNN模型准确率达88%，优于现有基准
- 适合海洋保护与智能监测领域研究者参考
珊瑚礁支撑大量海洋生物，是海岸防灾的重要屏障，属于关键海洋生态系统。但受污染、海洋酸化及海水温度异常威胁日益严重，高效保护与监测迫在眉睫。本研究提出基于多样化全球数据集的机器学习珊瑚白化分类系统，涵盖深海、沼泽、近岸等不同环境下的健康与白化珊瑚样本。对三种前沿模型——残差网络（ResNet）、视觉变换器（ViT）和卷积神经网络（CNN）——进行基准测试与比较。经全面超参数调优后，CNN模型达到最高准确率88%，超越现有基准。研究为自主珊瑚监测提供重要洞见，并对主流计算机视觉模型进行了综合分析。
原文摘要 · Abstract (English)
Coral reefs support numerous marine organisms and are an important source of coastal protection from storms and floods, representing a major part of marine ecosystems. However coral reefs face increasing threats from pollution, ocean acidification, and sea temperature anomalies, making efficient protection and monitoring heavily urgent. Therefore, this study presents a novel machine-learning-based coral bleaching classification system based on a diverse global dataset with samples of healthy and bleached corals under varying environmental conditions, including deep seas, marshes, and coastal zones. We benchmarked and compared three state-of-the-art models: Residual Neural Network (ResNet), Vision Transformer (ViT), and Convolutional Neural Network (CNN). After comprehensive hyperparameter tuning, the CNN model achieved the highest accuracy of 88%, outperforming existing benchmarks. Our findings offer important insights into autonomous coral monitoring and present a comprehensive analysis of the most widely used computer vision models.
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