EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation - Yang, Hechen; Li, Chen; Zhao, Xin; Cai, Bencheng; Zhang, Jiawei; Ma, Pingli; Zhao, Peng; Chen, Ao; Jiang, Tao; Sun, Hongzan; Teng, Yueyang; Qi, Shouliang; et al. (Frontiers in Microbiology, 2023)
Nowadays, the detection of environmental microorganism indicators is essential for us to assess the degree of pollution, but the traditional detection methods of aquatic organisms are accompanied by a large amount of carbon emissions, and this process consumes a lot of manpower and material resources. Therefore, it is necessary for us to make microbial data sets to be used in artificial intelligence. The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set that is applied in the field of multi-object detection of artificial intelligence. This method reduces the chemicals, manpower and equipment used in the process of detecting microorganisms. Meanwhile, it contributes to energy saving and emission reduction, and plays an important role in carbon neutralization and carbon peaking. EMDS-7 including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2365 images and 13216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-RCNN, YOLOv3, YOLOv4, SSD and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571