BM-UNet: lightweight multiclass-fire segmentation network and robust methods of aiming to the fire based on state-machine of mean segmentation mask and contour center-mass
|
Full Text |
Pdf
|
|
Author |
Vladimir Bochkov, Liliya Kataeva and Evgeniy Linev
|
|
e-ISSN |
1819-6608 |
|
On Pages
|
172-188
|
|
Volume No. |
19
|
|
Issue No. |
3
|
|
Issue Date |
March 30, 2024
|
|
DOI |
https://doi.org/10.59018/022430
|
|
Keywords |
image fire segmentation, BM-UNet, state machine.
|
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.
Back