Gas Leak Real-Time Detection and Volume Flow Quantification Based on Infrared Imaging and Advanced Algorithms

Due to the semi-transparent and irregular nature of gases, it is still a highly challenging task to effectively detect and quantify gas leaks especially those with small flow rates by only utilizing economical equipments. In this paper, we present a strategy for automating real-time identification a...

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Vydané v:IEEE access Ročník 13; s. 7284 - 7292
Hlavní autori: Yan, Man, Li, Zhou, Dong, Zheng, Liun, Yiming, Chen, Liyun, Wu, Xiaosong, Wu, Lijun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Cameras
Clustering
Clustering algorithms
DeeplabV3+ neural network
Flow velocity
Fluid flow
Gas flow
Gas identification
Gas lasers
gas quantification
Gases
Image edge detection
Image segmentation
Infrared cameras
Infrared imaging
Kmeans clustering algorithm
Optical filters
Optical flow (image analysis)
optical flow method
Real time
Real-time systems
Training
ISSN:2169-3536, 2169-3536
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Abstract Due to the semi-transparent and irregular nature of gases, it is still a highly challenging task to effectively detect and quantify gas leaks especially those with small flow rates by only utilizing economical equipments. In this paper, we present a strategy for automating real-time identification and quantification of gases in the mid-infrared band by combining an infrared camera combined with a series optimized algorithms. A basic network DeepLabV3+ is first modified by replacing its Xception backbone with MobileNetv2 for real-time gas detection and segmentation. Then special attention mechanisms tailored to the characteristics of the gas are added into the network to enhance the perception and recognition of the gas edges. The optimized Kmeans clustering algorithm is integrated to identify the Region of Interest (ROI) in the image containing the target gas. The quantification of the volume flow rate within the ROI is realized by integrating the radiation transfer model with the optical flow method. The experimental results indicate that the quantification limit of the gas flow rate can reach 0.01 L/min, which is comparable to that obtained by the methods with complicated instruments. Our detection and quantification strategy can find vast applications in hazardous gas monitoring field.
AbstractList Due to the semi-transparent and irregular nature of gases, it is still a highly challenging task to effectively detect and quantify gas leaks especially those with small flow rates by only utilizing economical equipments. In this paper, we present a strategy for automating real-time identification and quantification of gases in the mid-infrared band by combining an infrared camera combined with a series optimized algorithms. A basic network DeepLabV3+ is first modified by replacing its Xception backbone with MobileNetv2 for real-time gas detection and segmentation. Then special attention mechanisms tailored to the characteristics of the gas are added into the network to enhance the perception and recognition of the gas edges. The optimized Kmeans clustering algorithm is integrated to identify the Region of Interest (ROI) in the image containing the target gas. The quantification of the volume flow rate within the ROI is realized by integrating the radiation transfer model with the optical flow method. The experimental results indicate that the quantification limit of the gas flow rate can reach 0.01 L/min, which is comparable to that obtained by the methods with complicated instruments. Our detection and quantification strategy can find vast applications in hazardous gas monitoring field.
Author Dong, Zheng
Yan, Man
Wu, Xiaosong
Liun, Yiming
Chen, Liyun
Wu, Lijun
Li, Zhou
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Snippet Due to the semi-transparent and irregular nature of gases, it is still a highly challenging task to effectively detect and quantify gas leaks especially those...
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SubjectTerms Accuracy
Algorithms
Cameras
Clustering
Clustering algorithms
DeeplabV3+ neural network
Flow velocity
Fluid flow
Gas flow
Gas identification
Gas lasers
gas quantification
Gases
Image edge detection
Image segmentation
Infrared cameras
Infrared imaging
Kmeans clustering algorithm
Optical filters
Optical flow (image analysis)
optical flow method
Real time
Real-time systems
Training
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Title Gas Leak Real-Time Detection and Volume Flow Quantification Based on Infrared Imaging and Advanced Algorithms
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