EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation
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 inte...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1084312/full |
| _version_ | 1828012067123101696 |
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| author | Hechen Yang Chen Li Xin Zhao Bencheng Cai Jiawei Zhang Pingli Ma Peng Zhao Ao Chen Tao Jiang Tao Jiang Hongzan Sun Yueyang Teng Shouliang Qi Xinyu Huang Marcin Grzegorzek Marcin Grzegorzek |
| author_facet | Hechen Yang Chen Li Xin Zhao Bencheng Cai Jiawei Zhang Pingli Ma Peng Zhao Ao Chen Tao Jiang Tao Jiang Hongzan Sun Yueyang Teng Shouliang Qi Xinyu Huang Marcin Grzegorzek Marcin Grzegorzek |
| author_sort | Hechen Yang |
| collection | DOAJ |
| description | 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 Environmental Microorganism (EM) images 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 (Faster-Region Convolutional Neural Network (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. |
| first_indexed | 2024-04-10T09:25:52Z |
| format | Article |
| id | doaj.art-cb1e9af651df4be38f1a805731c3f3c2 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-10T09:25:52Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-cb1e9af651df4be38f1a805731c3f3c22023-02-20T05:19:42ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-02-011410.3389/fmicb.2023.10843121084312EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluationHechen Yang0Chen Li1Xin Zhao2Bencheng Cai3Jiawei Zhang4Pingli Ma5Peng Zhao6Ao Chen7Tao Jiang8Tao Jiang9Hongzan Sun10Yueyang Teng11Shouliang Qi12Xinyu Huang13Marcin Grzegorzek14Marcin Grzegorzek15Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaSchool of Resources and Civil Engineering, Northeastern University, Shenyang, ChinaSchool of Resources and Civil Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaSchool of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaInternational Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, ChinaShengjing Hospital, China Medical University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaInstitute of Medical Informatics, University of Lübeck, Lübeck, GermanyInstitute of Medical Informatics, University of Lübeck, Lübeck, GermanyDepartment of Knowledge Engineering, University of Economics in Katowice, Katowice, PolandNowadays, 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 Environmental Microorganism (EM) images 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 (Faster-Region Convolutional Neural Network (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.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1084312/fullenvironmental microorganismimage dataset constructionimage analysismultiple object detectiondeep learning |
| spellingShingle | Hechen Yang Chen Li Xin Zhao Bencheng Cai Jiawei Zhang Pingli Ma Peng Zhao Ao Chen Tao Jiang Tao Jiang Hongzan Sun Yueyang Teng Shouliang Qi Xinyu Huang Marcin Grzegorzek Marcin Grzegorzek EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation Frontiers in Microbiology environmental microorganism image dataset construction image analysis multiple object detection deep learning |
| title | EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation |
| title_full | EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation |
| title_fullStr | EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation |
| title_full_unstemmed | EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation |
| title_short | EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation |
| title_sort | emds 7 environmental microorganism image dataset seventh version for multiple object detection evaluation |
| topic | environmental microorganism image dataset construction image analysis multiple object detection deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1084312/full |
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