对比三种模型，选出最适合水下海带林分割的最优方案。
Comparative Assessment of Deep Learning Architectures for Underwater Subsurface Kelp Forest Segmentation with The Kelp-o-Tron
- 用三个深度学习模型对比分析水下海带分割效果。
- DeepLabV3在独立测试中达最高Dice(0.7120)和IoU(0.6267)。
- 提出可复用的数据集与评估流程，适合生态监测研究者。
水下海带林是重要的近岸生态系统，支持海洋生物多样性和生态动态，但因光学退化、光照变化、浑浊度、植被重叠及复杂底质背景，准确分割仍具挑战。本研究系统评估了三种深度学习语义分割框架：ResNet34-U-Net、ResNet50-DeepLabV3 和混合的 ResNet50-ASPP-Transformer，基于来自美国东北部海岸水域的高分辨率水下RGB图像进行海带检测。构建了包含3,395对图像-掩码的标注数据集用于训练与验证，并采用地理上独立的站点进行定量与定性评估。所有模型使用一致的预处理、增强与评估协议。在独立测试数据上，ResNet50-DeepLabV3取得最高Dice（0.7120）和交并比（IoU；0.6267），其次为ResNet34 U-Net（Dice 0.6868；IoU 0.5978）。混合ASPP Transformer虽达到最高像素准确率（0.8528），但Dice（0.6437）和IoU（0.5746）较低。外部定性评估显示，DeepLabV3在不同环境条件、图像质量与底质生境下分割更一致。总体而言，ResNet50-DeepLabV3（命名为Kelp-O-Tron）在精度、鲁棒性与泛化能力间表现最佳。该数据集、标注流程与对比评估为自动化水下栖息地制图与生态监测提供了可用资源。
原文摘要 · Abstract (English)
Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation remains challenging due to optical degradation, illumination variability, turbidity, overlapping vegetation, and complex benthic backgrounds. We systematically evaluated three deep learning semantic segmentation frameworks, ResNet34-U-Net, ResNet50-DeepLabV3, and a hybrid ResNet50-ASPP-Transformer architecture, for kelp detection using high-resolution underwater RGB imagery collected from northeastern U.S. coastal waters. A dataset of 3,395 SSeg assisted annotated image-mask pairs was developed for model training and validation, while geographically independent sites were used for quantitative and qualitative evaluation. All models used consistent preprocessing, augmentation, and evaluation protocols. On independent test data, ResNet50-DeepLabV3 achieved the highest Dice (0.7120) and Intersection over Union (IoU; 0.6267), followed by ResNet34 U Net (Dice 0.6868; IoU 0.5978). The hybrid ASPP Transformer achieved the highest pixel accuracy (0.8528) but lower Dice (0.6437) and IoU (0.5746). External qualitative evaluation further showed that DeepLabV3 produced more consistent segmentation across varying environmental conditions, image qualities, and benthic habitats. Overall, ResNet50-DeepLabV3, termed Kelp-O-Tron, provided the best balance of segmentation accuracy, robustness, and generalization. The dataset, annotation workflow, and comparative evaluation provide resources for advancing automated underwater habitat mapping and ecological monitoring.
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