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

Abstract

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.

Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis

“…These limitations invariably exert a negative influence on the outcomes of experiments and analytical processes. On the brighter side, advancements in the field are continually being made, as exemplified by the recent public release of the Environmental Microorganism Image Data set Seventh Version (EMDS-7) in 2021 . The continuous advancements in the field reflect a concerted effort to address these issues …”

“…On the brighter side, advancements in the field are continually being made, as exemplified by the recent public release of the Environmental Microorganism Image Data set Seventh Version (EMDS-7) in 2021. 65 The continuous advancements in the field reflect a concerted effort to address these issues. 65 Zhang et al 61 summarized some of the popular approaches for microorganism counting, including image processing and feature extractions, which are shown in Figure 3.…”

“…65 The continuous advancements in the field reflect a concerted effort to address these issues. 65 Zhang et al 61 summarized some of the popular approaches for microorganism counting, including image processing and feature extractions, which are shown in Figure 3. According to the review, microorganism counting involves separating adherent colonies and counting them.…”

Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements

“…However, they also pose risks in the form of pathogenic organisms that cause infectious diseases [14]. Traditional methods for studying microorganisms, like culture techniques and manual microscopy, can be time-consuming, costly, and occasionally inadequate due to morphological similarities between different species [15]. Consequently, the development of intelligent image recognition tools that can automate microorganism classification processes with minimal human intervention has become increasingly pertinent.…”