Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Vision for Natural Gas Methane Emissions Detection Using an
  Infrared Camera   It is crucial to reduce natural gas methane emissions, which can potentially
offset the climate benefits of replacing coal with gas. Optical gas imaging
(OGI) is a widely-used method to detect methane leaks, but is labor-intensive
and cannot provide leak detection results without operators' judgment. In this
paper, we develop a computer vision approach to OGI-based leak detection using
convolutional neural networks (CNN) trained on methane leak images to enable
automatic detection. First, we collect ~1 M frames of labeled video of methane
leaks from different leaking equipment for building CNN model, covering a wide
range of leak sizes (5.3-2051.6 gCH4/h) and imaging distances (4.6-15.6 m).
Second, we examine different background subtraction methods to extract the
methane plume in the foreground. Third, we then test three CNN model variants,
collectively called GasNet, to detect plumes in videos taken at other pieces of
leaking equipment. We assess the ability of GasNet to perform leak detection by
comparing it to a baseline method that uses optical-flow based change detection
algorithm. We explore the sensitivity of results to the CNN structure, with a
moderate-complexity variant performing best across distances. We find that the
detection accuracy can reach as high as 99%, the overall detection accuracy can
exceed 95% for a case across all leak sizes and imaging distances. Binary
detection accuracy exceeds 97% for large leaks (~710 gCH4/h) imaged closely
(~5-7 m). At closer imaging distances (~5-10 m), CNN-based models have greater
than 94% accuracy across all leak sizes. At farthest distances (~13-16 m),
performance degrades rapidly, but it can achieve above 95% accuracy to detect
large leaks (>950 gCH4/h). The GasNet-based computer vision approach could be
deployed in OGI surveys to allow automatic vigilance of methane leak detection
with high detection accuracy in the real world.

32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom