An improved YOLOv5-based microscopic examination method for activated sludge microorganisms

ZHANG Yuhang; YUAN Mingzhe; WANG Wenhong; ZHANG Jia; XIAO Jinchao; CAO Feidao

doi:10.13205/j.hjgc.202506019

Volume 43 Issue 6

Jun. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(6): 188-196

ZHANG Yuhang, YUAN Mingzhe, WANG Wenhong, ZHANG Jia, XIAO Jinchao, CAO Feidao. An improved YOLOv5-based microscopic examination method for activated sludge microorganisms[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(6): 188-196. doi: 10.13205/j.hjgc.202506019

Citation:

ZHANG Yuhang, YUAN Mingzhe, WANG Wenhong, ZHANG Jia, XIAO Jinchao, CAO Feidao. An improved YOLOv5-based microscopic examination method for activated sludge microorganisms[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(6): 188-196. doi: 10.13205/j.hjgc.202506019

Citation:

ZHANG Yuhang, YUAN Mingzhe, WANG Wenhong, ZHANG Jia, XIAO Jinchao, CAO Feidao. An improved YOLOv5-based microscopic examination method for activated sludge microorganisms[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(6): 188-196. doi: 10.13205/j.hjgc.202506019

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An improved YOLOv5-based microscopic examination method for activated sludge microorganisms

doi: 10.13205/j.hjgc.202506019

1. School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China;
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
3. Guangzhou Industrial Intelligence Research Institute, Guangzhou 511458, China

Received Date: 2024-09-28
Accepted Date: 2024-12-10
Rev Recd Date: 2024-11-08

Abstract

Abstract

To address the research shortages， as well as the low accuracy and high missed detection rate of existing activated sludge microbial detection algorithms， an improved YOLOv5-based microbial detection method was proposed. The K-Means++ algorithm was used to generate the most suitable anchor frame group for this dataset. The original C3 module in the YOLOv5 backbone network was replaced with the C3GC module to enhance feature extraction. In addition， the feature pyramid fusion coordinate attention mechanism in the neck network and the global-to-spatial aggregation module were employed to strengthen the fusion of feature information. Based on the actual collected microbial data of activated sludge， the training and test datasets were constructed using a data augmentation method. The improved algorithm achieved a recall rate of 97.4%， an average accuracy of 99.2%， and a model size of only 23.5MB on the test dataset， which demonstrated a certain improvement in accuracy and regression rate compared to the original YOLOv5 model and other mainstream detection models and variants， while meeting the requirements for rapid deployment on mobile terminals. The experimental data showed that the improved model sacrificed a small amount of detection speed in exchange for a significant improvement in accuracy and regression rate， proving the effectiveness of the improvement. This study provides a valuable reference for the development of artificial intelligence in the field of environmental governance and serves as an example for the advancement of intelligent equipment.
- sewage treatment,
- microbial detection,
- YOLOv5,
- characteristic pyramids,
- coordinate attention mechanism,
- global-to-spatial aggregation module

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References(19)

References

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