Work

An SFN-YOLO-based detection method for the fermentation stage in the anaerobic digestion process

Year: 2025

Type: article

Abstract: Anaerobic digestion is an effective and low-carbon approach for biomass utilization and energy recovery. To accurately determine fermentation stages and enhance process control, this paper proposes an anaerobic digestion fermentation stage detection method based on SFN-YOLO. This model combines spatial feature interaction fusion pyramid network (SFIFPN), a focused linear attention (FLAtt) mechanism that concentrates on key features, and a normalized Wasserstein distance (NWD) loss for robust bounding box regression to accurately detect and classify anaerobic digestion images at different stages. The model demonstrates good detection ability on the validation dataset (precision reaches 91.9%, recall reaches 77.6%, and [email protected] reaches 81.1%), with the total proportion of correctly detected boxes reaching as high as 84.27%. This work demonstrates the potential of intelligent visual monitoring to improve the accuracy, efficiency, and automation of anaerobic digestion processes, contributing to sustainable bioenergy management.

Source: iScience

DOI: 10.1016/j.isci.2025.114243

Authors: Peng Liu, Shengxian Cao, Gong Wang, Siyuan Fan, Tianyi Sun, An Yan

Institution: Northeast Electric Power University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,240

Funders: Department of Science and Technology of Jilin Province, Jilin Scientific and Technological Development Program