Detecting Natural Gas Leaks Using Digital Images and Novelty Filters

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

doi:10.1007/978-3-642-31368-4_29

Cited by 4 publications

(8 citation statements)

References 10 publications

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“…In [15], [16] the authors described a first natural gas leak detection system, using production head images, based on a surveillance system (CCTV). This system did not need installation of specific sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Vector ̂∈ is the component of that is "known" by the subspace and can be defined as a linear combination of { 1 , 2 , … }. Vector ̃⊥ is the new information, that is unknown by the subspace and cannot be represented as a linear combination of { 1 , 2 , … } [15]. In this method, the magnitude of the vector ̃, ‖̃‖, is considered a measure of dissimilarity, that is, the higher the value ‖̃‖, the greater the probability that an image is not part of subspace .…”

Section: Related Workmentioning

confidence: 99%

“…Only two previously works, that we published in literature [15], [16], used visible image processing to detect gas leakage. These works employed the novelty filter classifier.…”

Section: Introductionmentioning

confidence: 99%

“…To compare the results of this new method with those obtained by previous studies that used the novelty classifier [15], [16], this work uses the same image dataset of these studies.…”

Section: Introductionmentioning

confidence: 99%

“…-Compare the best performance model with the results obtained with TL, using AlexNet, DenseNet-201, GoogLeNet, MobileNet-v2, ResNet-18 and VGG-16. -Compare the performance of the CNN based model with the previous novelty filter based model [15], [16]. -Provide a visual explanation of gas leakage, illustrating the regions of the image that were relevant to the "with leak" class label.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Only two previously works, that we published in literature [15], [16], used visible image processing to detect gas leakage. These works employed the novelty filter classifier.…”

Section: Introductionmentioning

confidence: 99%

“…To compare the results of this new method with those obtained by previous studies that used the novelty classifier [15], [16], this work uses the same image dataset of these studies.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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Using Digital Image Processing and a Novelty Classifier for Detecting Natural Gas Leaks

Melo

Costa

Filho

2014

Intelligent Systems for Science and Information

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A new method for recognizing hand configurations of Brazilian gesture language

Filho

Santos

Souza

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This paper describes a new method for recognizing hand configurations of the Brazilian Gesture Language - LIBRAS - using depth maps obtained with a Kinect® camera. The proposed method comprised three phases: hand segmentation, feature extraction, and classification. The segmentation phase is independent from the background and depends only on pixel depth information. Using geometric operations and numerical normalization, the feature extraction process was done independent from rotation and translation. The features are extracted employing two techniques: (2D)2LDA and (2D)2PCA. The classification is made with a novelty classifier. A robust database was constructed for classifier evaluation, with 12,200 images of LIBRAS and 200 gestures of each hand configuration. The best accuracy obtained was 95.41%, which was greater than previous values obtained in the literature.

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Detecting Natural Gas Leaks Using Digital Images and Novelty Filters

Cited by 4 publications

References 10 publications

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

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

Using Digital Image Processing and a Novelty Classifier for Detecting Natural Gas Leaks

A new method for recognizing hand configurations of Brazilian gesture language

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