System, method and device for detection and analysis of wildfire smoke

A method for detection and analysis of wildfire smoke using artificial intelligence, the method comprising:

• receiving collected image data from an imaging device;
• filtering the collected image data based on predetermined filtration criteria;
• adjusting an allocation of processing resources based on an amount of the filtered image data;
• prioritizing the filtered image data based on predetermined priority criteria to generate prioritized image data;
• dividing each image frame in the prioritized image data into a plurality of grid cells;
• determining changes between consecutive image frames of the prioritized image data by comparing pixels within each grid cell of the plurality of grid cells of the consecutive image frames;
• in response to detecting changes indicative of smoke, calculating a density of pixel changes, wherein grid cells having a density below a predetermined density threshold are discarded;
• identifying and removing irrelevant objects from the prioritized image data;
• analyzing the prioritized image data using a smoke detection model trained to detect smoke, the smoke detection model comprising a machine-learning-based pattern detection model configured to receive the prioritized image data as an input and generate a score describing a smoke detection as an output.

A device for detection and analysis of wildfire smoke using artificial intelligence, the device comprising:

• a network interface;
• a processor;
• and
• a non-transitory computer readable memory having stored thereon instructions that, when executed by the processor, cause the device to:
• receive collected image data from an imaging device;
• filter the collected image data based on predetermined filtration criteria;
• adjust an allocation of processing resources based on an amount of the filtered image data;
• prioritize the filtered image data based on predetermined priority criteria to generate prioritized image data;
• divide each image frame in the prioritized image data into a plurality of grid cells;
• determine changes between consecutive image frames of the prioritized image data by comparing pixels within each grid cell of the plurality of grid cells of the consecutive image frames;
• in response to detecting changes indicative of smoke, calculate a density of pixel changes, wherein grid cells having a density below a predetermined density threshold are discarded;
• identify and remove irrelevant objects from the prioritized image data;
• analyze the prioritized image data using a smoke detection model trained to detect smoke, the smoke detection model comprising a machine-learning-based pattern detection model configured to receive the prioritized image data as an input and generate a score describing a smoke detection as an output.