SeagrassFinder：面向真实场景大叶藻检测与覆盖度估计的深度学习
SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild
AI总结：
本文提出 SeagrassFinder，构建8,300余张标注水下图像数据集，利用迁移学习比较多种深度模型并结合图像增强实现大叶藻检测，AUROC超过0.95，同时提出视频覆盖度估计方法。
摘要：
海草床在海洋生态系统中发挥着关键作用，可带来碳封存、水质改善和栖息地提供等益处。监测海草的分布与丰度对于环境影响评估和保护工作至关重要。然而，目前通过分析水下视频数据来评估海草覆盖度的人工方法既耗时又主观。本工作探索利用深度学习模型，从水下视频数据中自动化完成海草检测与覆盖度估计流程。我们构建了一个包含超过8,300张已标注水下图像的新数据集，随后通过迁移学习评估了多种深度学习架构，包括 ResNet、InceptionNetV3、DenseNet 和 Vision Transformer，用于判断海草存在与否的二分类任务。结果表明，深度学习模型，尤其是 Vision Transformer，在预测大叶藻存在方面能够取得高性能，在最终测试数据集上的 AUROC 分数超过0.95。应用水下图像增强进一步提升了模型的预测能力。此外，我们提出了一种从视频数据估计海草覆盖度的新方法，其初步结果令人鼓舞，并与专家人工标注相一致，表明该方法具有实现一致且可扩展监测的潜力。所提出的方法能够高效处理大量视频数据，与现有人工方法相比，可以获得关于海草分布的更详细信息。由于海草是沿海生态系统健康的重要指标，这些信息对于环境影响评估和监测计划至关重要。本项目展示了深度学习能够为海洋生态学和环境监测领域带来的价值。
英文摘要：
Seagrass meadows play a crucial role in marine ecosystems, providing benefits such as carbon sequestration, water quality improvement, and habitat provision. Monitoring the distribution and abundance of seagrass is essential for environmental impact assessments and conservation efforts. However, the current manual methods of analyzing underwater video data to assess seagrass coverage are time-consuming and subjective. This work explores the use of deep learning models to automate the process of seagrass detection and coverage estimation from underwater video data. We create a new dataset of over 8,300 annotated underwater images, and subsequently evaluate several deep learning architectures, including ResNet, InceptionNetV3, DenseNet, and Vision Transformer for the task of binary classification on the presence and absence of seagrass by transfer learning. The results demonstrate that deep learning models, particularly Vision Transformers, can achieve high performance in predicting eelgrass presence, with AUROC scores exceeding 0.95 on the final test dataset. The application of underwater image enhancement further improved the models' prediction capabilities. Furthermore, we introduce a novel approach for estimating seagrass coverage from video data, showing promising preliminary results that align with expert manual labels, and indicating potential for consistent and scalable monitoring. The proposed methodology allows for the efficient processing of large volumes of video data, enabling the acquisition of much more detailed information on seagrass distributions in comparison to current manual methods. This information is crucial for environmental impact assessments and monitoring programs, as seagrasses are important indicators of coastal ecosystem health. This project demonstrates the value that deep learning can bring to the field of marine ecology and environmental monitoring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
