BM-unet: Lightweight multiclass-fire segmentation network and robust methods of aiming the fire based on the state-machine of mean segmentation mask and contour center-mass

Vladimir Bochkov; Liliya Kataeva; Evgeniy Linev

Author(s)

Vladimir Bochkov Alexeev State Technical University,,Russia

Liliya Kataeva Alexeev State Technical University,,Russia

Evgeniy Linev Alexeev State Technical University,,Russia

Abstract

The article presents the BM-UNet neural network architecture optimized for mobile devices. The article focuses on the possibility of real-time operation, takes into account various techniques for lightening the model, and provides a comparison with UNet-half. An improved model in terms of performance/accuracy ratio is applied in the algorithm of frame-by-frame segmentation of the flame on video, the result of which is averaged, and the optimal extinguishing point is found. For the latter, an approach to organizing a finite state machine is presented for switching between time-averaging windows for the possibility of timely response and minimization of mechanical losses of targeting. Combined, the method of frame-by-frame segmentation of flame contours and the algorithm for finding the center-mass point can be used in robotic flame extinguishing systems.

Publication Details

Page(s) 172-188

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 3, Year: 2024

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Keywords

State machine, BM-UNet, image fire segmentation

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