Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera

Wang, Jingfan; Tchapmi, Lyne P.; Ravikumara, Arvind P.; McGuire, Mike; Bell, Clay S.; Zimmerle, Daniel; Savarese, Silvio; Brandt, Adam R.

doi:10.48550/arxiv.1904.08500

Cited by 2 publications

(3 citation statements)

References 40 publications

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“…Additionally, it is important to monitor for CO 2 leaks [433]. ML techniques have recently been applied to monitoring potential CO 2 leaks from wells [434]; computer vision approaches for emissions detection (see [435] and §5.1) may also be applicable.…”

Section: Sequestering Comentioning

confidence: 99%

Tackling Climate Change with Machine Learning

Rolnick¹,

Donti²,

Kaack³

et al. 2019

Preprint

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

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Section: Sequestering Comentioning

confidence: 99%

Tackling Climate Change with Machine Learning

Rolnick¹,

Donti²,

Kaack³

et al. 2019

Preprint

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“…Vision-based gas detection, including objects such as smoke and fire, has been an active research topic for decades. In [8], background subtraction and classification CNN are combined to detect the leakage of methane. Background subtraction is used to extract gas flow images.…”

Section: Vision-based Gas Detectionmentioning

confidence: 99%

“…OGI technique utilizes mid-wave infrared cameras to visualize VOC gases to help people find the leakage of VOC gas. With recent progress made in computer vision area, automatic gas detection technique [8] [9] [10] is often attached to OGI systems to save labor costs. A typical OGI system is depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

VOC gas leakage detection using infrared image and convolutional neural networks

Zhang

Chen

et al. 2023

AOPC 2022: Infrared Devices and Infrared Technology; And Terahertz Technology and Applications

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Leakage of volatile organic compounds (VOC) gas is one of the main sources of air pollution, and it poses a great threat to health and safety in many ways. Optical gas imaging (OGI) technique utilizes mid-wave infrared camera to visualize VOC gas and helps people observe the leakage of VOC gas. In this paper, we propose a novel method that utilizes deep learning technique and convolutional neural networks to detect the leakage of VOC gas from single-frame mid-wave infrared image. The proposed method consists of three components: color transformation pre-processing unit, feature extraction networks, and single-stage object detection sub-networks. Location-aware deformable convolution, which adjusts its sampling grid to fit the ever-changing shape of VOC gases, is employed for better feature extraction. Besides, a new loss function called leakage center loss is introduced to estimate where leakage comes from, and it forces the network to pay more attention to leakage center where the density of VOC gases is higher than dissipated parts. The proposed method is evaluated using a self-collected dataset where thousands of gas images are captured and annotated. Experimental results show that location-aware deformable convolution contributes to around 7% mAP improvement, while leakage center loss contributes to around 4% mAP improvement. Finally, our method achieves 81% mAP, which is better than existing general-purpose object detection methods. By simplifying the network architecture, our proposed method can also be implemented on embedded system for handheld VOC leakage detection devices.

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Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera

Cited by 2 publications

References 40 publications

Tackling Climate Change with Machine Learning

Tackling Climate Change with Machine Learning

VOC gas leakage detection using infrared image and convolutional neural networks

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