Leak Detection of Natural Gas with Base on the Components of Color Spaces RGB and HSI Using Novelty Filter

Melo, Roberlanio Oliveira; Filho, Cícero Ferreira Fernandes Costa; Costa, Marly Guimarães Fernandes

doi:10.1109/tla.2014.7014528

Cited by 4 publications

(1 citation statement)

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“…Thermal imaging has been introduced to detect gas-stressed plants because their stomatal activity deviated from corresponding unstressed conditions, forming a temperature difference in the plant canopy [13]. RGB imaging can also capture the color alteration of the plants associated with gas leakage [14]. The two techniques were combined to detect the plant stress associated with water deficit [5].…”

Section: Introductionmentioning

confidence: 99%

Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection

Ma,

Zhuo,

Chen

et al. 2024

Remote Sensing

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Remote sensing detection of natural gas leaks remains challenging when using ground vegetation stress to detect underground pipeline leaks. Other natural stressors may co-present and complicate gas leak detection. This study explores the feasibility of identifying and distinguishing gas-induced stress from other natural stresses by analyzing the hyperspectral reflectance of vegetation. The effectiveness of this discrimination is assessed across three distinct spectral ranges (VNIR, SWIR, and Full spectra). Greenhouse experiments subjected three plant species to controlled environmental stressors, including gas leakage, salinity impact, heavy-metal contamination, and drought exposure. Spectral curves obtained from the experiments underwent preprocessing techniques such as standard normal variate, first-order derivative, and second-order derivative. Principal component analysis was then employed to reduce dimensionality in the spectral feature space, facilitating input for linear/quadratic discriminant analysis (LDA/QDA) to identify and discriminate gas leaks. Results demonstrate an average accuracy of 80% in identifying gas-stressed plants from unstressed ones using LDA. Gas leakage can be discriminated from scenarios involving a single distracting stressor with an accuracy ranging from 76.4% to 84.6%, with drought treatment proving the most successful. Notably, first-order derivative processing of VNIR spectra yields the highest accuracy in gas leakage detection.

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Section: Introductionmentioning

confidence: 99%

Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection

Ma,

Zhuo,

Chen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images

2020

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Detecting natural gas leaks is one of the most important measures in the oil industry for preventing accidents. The literature provides different techniques for detecting natural gas leaks. However, except for previous studies by the authors on this topic, there remains a gap in the literature on leak detection of natural gas using digital images, without the need for sensors or special cameras calibrated for the spectrum of methane molecules. These previous studies used image-processing techniques associated with a novelty filter classifier to detect the presence or absence of visible cloud of hydrocarbon vapors, that is, a natural gas plume in Closed Circuit Television (CCTV) frames installed in onshore wellheads. In this paper, we present a new method for detecting natural gas leaks in oil facilities that enhances the results obtained previously, along with the Gradient-weighted Class Activation Mapping Algorithm (Grad-CAM) to identify natural gas leaks. In this new method, convolutional neural networks (CNN) are applied to classify images (CCTV frames) as belonging to classes with or without natural gas leaks in onshore wellheads. Experimental results showed that the best performance model presented an accuracy of 99.78% and false negative rate of 0.00%.

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An Improved GMM-Based Algorithm With Optimal Multi-Color Subspaces for Color Difference Classification of Solar Cells

Chen

et al. 2018

IEEE Trans. Semicond. Manufact.

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Leak Detection of Natural Gas with Base on the Components of Color Spaces RGB and HSI Using Novelty Filter

Cited by 4 publications

References 0 publications

Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection

Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection

Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images

An Improved GMM-Based Algorithm With Optimal Multi-Color Subspaces for Color Difference Classification of Solar Cells

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