Accurate Multiclass Fire Segmentation: Approaches, Neural Networks, and Segmentation Schemes

This 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 multiwindow schemes for partitioning the frame into a grid of subareas) segmentation schemes are proposed. The best segmentation methods and schemes in terms of quality are selected.