AUTHOR=Yang Hechen , Li Chen , Zhao Xin , Cai Bencheng , Zhang Jiawei , Ma Pingli , Zhao Peng , Chen Ao , Jiang Tao , Sun Hongzan , Teng Yueyang , Qi Shouliang , Huang Xinyu , Grzegorzek Marcin 

TITLE=EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

JOURNAL=Frontiers in Microbiology

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1084312

DOI=10.3389/fmicb.2023.1084312

ISSN=1664-302X

ABSTRACT=<p>Nowadays, the detection of environmental microorganism indicators is essential for us to assess the degree of pollution, but the traditional detection methods consume 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. EMDS-7 including the original <italic>Environmental Microorganism (EM) images</italic> 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 2,65 images and 13,216 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 (<italic>Faster-Region Convolutional Neural Network</italic> (Faster-RCNN), YOLOv3, YOLOv4, SSD, and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: <ext-link ext-link-type="uri" xlink:href="https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571" xmlns:xlink="http://www.w3.org/1999/xlink">https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571</ext-link>.</p>