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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19455
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Total Authors : 21252
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Published Paper Details
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| Paper Title: | Fire Detection and Warning Application From Images and Videos using Deep Learning |
| Authors Name: | Dr. S. Yasotha , Avinash , Akash Prajapati , Ashish Kumar Verma , Aryan Rajvansh |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_181797
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| Published Paper Id: | IJRTI2205012 |
| Published In: | Volume 7 Issue 5, May-2022 |
| DOI: | |
| Abstract: | To address the current problem, a number of fire picture organization options have been offered; Most of these rely on rules-based processes or high-quality elements. Propose a novel, deep convolutional neural network (CNN) computation for high-precision fire picture recognition. Use adaptable piecemeal direct units in the secret layers of the organization, not the traditional straight straight units or resolving abilities of older techniques. Create a second small dataset of fire photos to help us prepare and test our model. To address the issue of overfitting caused by limited dataset preparation by an organization using traditional information extension methods and generative adversarial organizations to operate on the amount of initial photographs available. This research examines handcrafted drawings in the light of fire detection rules. From 500 forest images taken under different imaging settings. Non-fire pixels are distinguished by the light force of a viable photograph, while fire pixels are distinguished by the shading appearance of fire or fire and the existence of fire. This representation allows a class-by-class examination of the performance of each standard. It is demonstrated that current writing ideas and processes are class-dependent, with none of them performing equally well across all classifications. Meanwhile, a recently proposed strategy, based on AI methods and incorporating all the highlighted parameters, overcomes existing state-of-the-art writing processes in various classes. This technology ensures exciting advances in determining the fate of metrologic devices for detecting fire in any setting. Fire detection, deep learning, fire and non fire are all index terms. |
| Keywords: | Deep Learning, Fire Detection, Machine Learning, Multi-layer receptor (MLP) |
| Cite Article: | "Fire Detection and Warning Application From Images and Videos using Deep Learning ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.89 - 93, May-2022, Available :http://www.ijrti.org/papers/IJRTI2205012.pdf |
| Downloads: | 000205250 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2205012
Registration ID:181797
Published In: Volume 7 Issue 5, May-2022
DOI (Digital Object Identifier):
Page No: 89 - 93 Country: Coimbatore, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2205012 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2205012 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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