Journal of Applied Science and Engineering

Published by Tamkang University Press

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Enhancing Coal-Fired Power Plant Efficiency through Edge Computing and Video Processing for Real-Time Emission Monitoring

 2026

 Volume 34

Lingyun Yang1, Minglong Wang1, Qianchuan Zhao2, Wenfeng Yang1, and Yang Li1

1GUIZHOU CASICloud-tech Co.,Ltd, Guiyang, Guizhou,550001, China

2Department of Automation, Tsinghua University,100084, China

Received: April 14, 2026
Accepted: May 16, 2026
Publication Date: August 12, 2026

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Real-time video emission monitoring based onedge-cloud collaboration

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202611_34.042  

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To address limitations in real-time responsiveness, spatial perception, and long-term stability in emission monitoring of coal-fired power plants, this paper proposes an edge–cloud collaborative video-based monitoring framework. The system integrates video acquisition, image preprocessing, multi-scale target detection, emission state recognition, and cloud-based collaborative analysis into a unified pipeline. An improved YOLO-FPN model is developed to enhance the detection of plume regions, flame instances, and abnormal operating zones. In addition, a multi-node edge collaboration strategy with dynamic model updating is introduced to reduce latency and improve adaptive performance over time. Experiments conducted on a real industrial testbed demonstrate improved detection accuracy, reduced processing delay, and lower network bandwidth consumption, while maintaining stable online deployment capability.

Keywords: Coal-fired power plants; edge-cloud collaboration; video monitoring; YOLO-FPN; edge computing; real-time emission monitoring; target detection

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