Abstract
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. A new dataset for marine microalgae detection is proposed in this paper. Six classes of microalgae commonlyfound in the ocean (Bacillariophyta, Chlorella pyrenoidosa, Platymonas, Dunaliella salina, Chrysophyta, Symbiodiniaceae) are microscopically imaged in real-time. Images of Symbiodiniaceae in three physiological states known as normal, bleaching, and translating are also included. We annotated these images with bounding boxes using Labelme software and split them into the training and testing sets. The total number of images in the dataset is 937 and all the objects in these images were annotated. The total number of annotated objects is 4201. The training set contains 537 images and the testing set contains 430 images. Baselines of different object detection algorithms are trained, validated and tested on this dataset. This data set can be got accessed via this http URL.
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URL
https://arxiv.org/abs/2211.07546
