视觉遇见藻类：一种用于微藻识别与健康监测的新方法
Vision meets algae: A novel way for microalgae recognization and health monitor
AI总结：
针对海洋微藻自动识别与监测问题，研究构建了包含不同属和状态的微藻图像数据集，并验证了TOOD、YOLOv5、YOLOv8及RCNN等模型在多目标检测中的有效性，为水质评价和实时检测提供了基础数据。
摘要：
海洋微藻在海洋中广泛分布并在生态系统中发挥着至关重要的作用。在显微图像中自动识别和定位海洋微藻，将有助于建立海洋生态环境监测和水质评价系统。我们提出了一个用于海洋微藻检测的新数据集以及一系列检测方法，该数据集包含了不同属的藻类图像以及同一属在不同状态下的图像。我们在数据集中设置了类别不平衡的数量，并在测试集中添加了混合水样图像，以模拟野外的实际情况。随后，我们在此数据集上训练、验证并测试了TOOD、YOLOv5、YOLOv8以及RCNN变体算法。结果表明，单阶段和双阶段目标检测模型均能实现较高的平均精度均值，这证明了计算机视觉在微藻多目标检测中的能力，并为微藻细胞的实时检测提供了基础数据和模型。
英文摘要：
Marine microalgae are widespread in the ocean and play a crucial role in the ecosystem. Automatic identification and location of marine microalgae in microscopy images would help establish marine ecological environment monitoring and water quality evaluation system. We proposed a new dataset for the detection of marine microalgae and a range of detection methods, the dataset including images of different genus of algae and the same genus in different states. We set the number of unbalanced classes in the data set and added images of mixed water samples in the test set to simulate the actual situation in the field. Then we trained, validated and tested the, TOOD, YOLOv5, YOLOv8 and variants of RCNN algorithms on this dataset. The results showed both one-stage and two-stage object detection models can achieve high mean average precision, which proves the ability of computer vision in multi-object detection of microalgae, and provides basic data and models for real-time detection of microalgal cells.
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。
