Email this article Accurate Multiclass Fire Segmentation: Approaches, Neural Networks, and Segmentation Schemes
- Authors: Bochkov V.S.1, Kataeva L.Y.1, Maslennikov D.A.1
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Affiliations:
- R. E. Alekseev Nizhny Novgorod State Technical University
- Issue: No 3 (2024)
- Pages: 71-86
- Section: Intelligent Planning and Control
- URL: https://journal-vniispk.ru/2071-8594/article/view/265360
- DOI: https://doi.org/10.14357/20718594240306
- EDN: https://elibrary.ru/JJLEUQ
- ID: 265360
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Abstract
The paper presents a solution to the problem of multiclass flame segmentation with separation by combustion color. The mathematical problems of partial (without separation of the background class into a separate component of the search vector) and full (with separation) segmentation are formulated. A comparison of convolutional neural network methods of UNet, Deeplab and their modern variations, including the wUUNet method developed specifically for the problem under consideration, is carried out. The paper emphasizes the influence of the size of the computation matrix of segmentation computations with the original frame. Both lossy (compressing the frame to the size of the computation matrix and then decompressing it into the original frame) and lossless (applying a single-window frame sizing scheme or multi-window schemes for partitioning the frame into a grid of sub-areas) segmentation schemes are proposed. The best segmentation methods and schemes in terms of quality are selected.
About the authors
Vladimir S. Bochkov
R. E. Alekseev Nizhny Novgorod State Technical University
Author for correspondence.
Email: vladimir2612@bk.ru
Postgraduate Student
Russian Federation, Nizhny NovgorodLilia Yu. Kataeva
R. E. Alekseev Nizhny Novgorod State Technical University
Email: kataeval2010@mail.ru
Doctor of Physical and Mathematical Sciences, Professor
Russian Federation, Nizhny NovgorodDmitry A. Maslennikov
R. E. Alekseev Nizhny Novgorod State Technical University
Email: dmitrymaslennikov@mail.ru
Candidate of Physical and Mathematical Sciences, Associate Professor
Russian Federation, Nizhny NovgorodReferences
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