Infrared image identification method of substation equipment fault under weak supervision Article Swipe
Anjali Sharma
,
Priya Banerjee
,
Nikhil Singh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.11214
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.11214
This study presents a weakly supervised method for identifying faults in infrared images of substation equipment. It utilizes the Faster RCNN model for equipment identification, enhancing detection accuracy through modifications to the model's network structure and parameters. The method is exemplified through the analysis of infrared images captured by inspection robots at substations. Performance is validated against manually marked results, demonstrating that the proposed algorithm significantly enhances the accuracy of fault identification across various equipment types.
Related Topics
Concepts
Identification (biology)
Fault (geology)
Infrared Spectrophotometry
Computer science
Artificial intelligence
Infrared
Fault detection and isolation
Robot
Pattern recognition (psychology)
Engineering
Computer vision
Reliability engineering
Seismology
Biology
Botany
Actuator
Chemistry
Optics
Chromatography
Physics
Geology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.11214
- https://arxiv.org/pdf/2311.11214
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388891061
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4388891061Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.11214Digital Object Identifier
- Title
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Infrared image identification method of substation equipment fault under weak supervisionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-11-19Full publication date if available
- Authors
-
Anjali Sharma, Priya Banerjee, Nikhil SinghList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.11214Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.11214Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2311.11214Direct OA link when available
- Concepts
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Identification (biology), Fault (geology), Infrared Spectrophotometry, Computer science, Artificial intelligence, Infrared, Fault detection and isolation, Robot, Pattern recognition (psychology), Engineering, Computer vision, Reliability engineering, Seismology, Biology, Botany, Actuator, Chemistry, Optics, Chromatography, Physics, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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