Abstract
This paper describes a solution of computer vision task concerning multiclass fire segmentation to get and show location of red, yellow and orange flame. We use UNet model as the best open-sourced convolutional neural network baseline. Based on this model we introduce UUNet-concatenative and wUUNet models. Since the multiclass fire segmentation task is solved for the first time in science, we collect the appropriate dataset and use the dataset-labeling alignment via look-up-tables. Also we compare models trained by Soft Dice and Jac-card indexes in combination with binary cross-entropy as a loss functions. Paper shows the problem of accuracy loss at bounding nodes of splitting the frame. As a solution we introduce combinational methods of partially intersected areas. The comparison of the used models and calculation schemes is demonstrated and the corresponding conclusions of the investigation are made.
Highlights
Significant attention has been devoted to the problem of in-time forest fire detection.Considering the rapid development of convolutional neural networks, as well as their universality and effectivity in comparison with classic algorithms, such methods can be applied to flame detection tasks as well as their initial purpose.A previous study [1] described a fire object-detection solution using a YOLOv2 [2]model to obtain a bounding box for areas of flame without concretization of its class
The first is that fire has a wide variety of available contour configurations and, unlike regular convex objects, such as automobiles and pedestrians, fire objects cannot be optimally inscribed into a bounding box, which leads to the large variance of mAP metric accuracy
Vanced driver-assistance systems), fire objects cannot be optimally inscrib bounding box, which leads to the large variance of mAP metric accuracy
Summary
Significant attention has been devoted to the problem of in-time forest fire detection. Model to obtain a bounding box for areas of flame without concretization of its class. The first sented via a two-step pipeline: obtaining regions of interest (ROIs) represent same color and fire recognition for each region Such methods have been des previous studies [3,4]. Recognition (B) of super-pixel flame areas, rep solve the problem of multiclass fire segmentation because. We suggest innovative methods to improve segmentation accuracy based on the composition of partially intersected areas via weighted addition and Gaussian mixtures of calculation results. This model is the combination of binary and multiclass UNet methods It enables the multiclass (differentiated by color) segmentation of signal obtained from the binary part of acquired single-nature objects (flame areas).
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