Harnessing the power of AutoML: A comparative study of image recognition techniques for smoking detection Article Swipe
Anbang Wang
,
Jianing Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.54254/2755-2721/33/20230246
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.54254/2755-2721/33/20230246
As infrastructure continues to evolve, the significance of fire protection escalates. Many fires are caused by smoking in smoke-free areas, underscoring the necessity to promptly detect smoking activities in hazardous zones. In this scenario, image recognition emerges as a pivotal tool. The accuracy and efficiency of image recognition bear substantial implications for both academic and industrial sectors, and these aspects form the crux of our investigation. This study aims to compare the performance of image recognition techniques based on automatic machine learning with those of traditional methods such as YOLO. Our findings indicate that image recognition powered by automatic machine learning outperforms YOLO recognition in terms of efficiency and accuracy.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.54254/2755-2721/33/20230246
- https://www.ewadirect.com/proceedings/ace/article/view/9965/pdf
- OA Status
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- References
- 16
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4391477300Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.54254/2755-2721/33/20230246Digital Object Identifier
- Title
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Harnessing the power of AutoML: A comparative study of image recognition techniques for smoking detectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-02Full publication date if available
- Authors
-
Anbang Wang, Jianing YuList of authors in order
- Landing page
-
https://doi.org/10.54254/2755-2721/33/20230246Publisher landing page
- PDF URL
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https://www.ewadirect.com/proceedings/ace/article/view/9965/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://www.ewadirect.com/proceedings/ace/article/view/9965/pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Image (mathematics), Machine learning, Power (physics), Computer vision, Pattern recognition (psychology), Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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16Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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