# US 2026/0094444 A1 · System, method and device for detection and analysis of wildfire smoke > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0094444 A1 - Document ID: US20260094444A1 - Status: Published application - Kind: A1 - Application number: 18/899,870 - Filed: 2024-09-27 - Published: 2026-04-02 - Earliest claimed: 2024-09-27 - Applicants: SenseNet Inc. - Inventors: Hamed Noori; Shahab Bahrami - Web page: https://patentgrep.com/patent/US20260094444A1 ## CPC - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06V 10/14: Optical characteristics of the device performing the acquisition or on the illumination arrangements - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V 10/751: {Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching} - G06V 20/38: {Outdoor scenes} - G06V 20/44: {Event detection} - G06V 2201/07: Target detection - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A system, method, and device for the detection and analysis of wildfire smoke are provided. The system includes an imaging device for collecting image data and a processing server including a queueing subsystem, a motion detection subsystem, and a smoke detection subsystem. The queueing subsystem includes an initial frame assessment module, a resource allocation module, and a queue management module. The motion detection subsystem includes a grid cell division module, a change detection module, and a density calculation module. The smoke detection subsystem includes a preliminary object filtering module, and a deep learning-based analysis module to analyze 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 image data as an input and generate a score describing a smoke detection as an output. ## Claims ### Claim 1 A system for detection and analysis of wildfire smoke using artificial intelligence, the system comprising: an imaging device configured to collect image data; a processing server for processing the collected image data, the processing server comprising: a queueing subsystem comprising: an initial frame assessment module configured to: receive the collected image data from the imaging device; filter the collected image data based on predetermined filtration criteria; a resource allocation module configured to adjust an allocation of processing resources based on an amount of the filtered image data; a queue management module configured to prioritize the filtered image data based on predetermined priority criteria to generate prioritized image data; a motion detection subsystem comprising: a grid cell division module configured to divide each image frame in the prioritized image data into a plurality of grid cells; a change detection module configured to 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; a density calculation and filtering module configured to, 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; and a smoke detection subsystem comprising: a preliminary object filtering module configured to identify and remove irrelevant objects from the prioritized image data; a deep learning-based analysis module configured to 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. ### Claim 2 (depends on 1) The system of claim 1, wherein the smoke detection subsystem further comprises an automated camera control module configured to, in response to detecting smoke, control the imaging device to focus on an area where the smoke detection occurred. ### Claim 3 (canceled) ### Claim 4 (depends on 1) The system of claim 1, wherein the predetermined filtration criteria include at least one of clarity, format, and integrity of the collected image data. ### Claim 5 (canceled) ### Claim 6 (depends on 1) The system of claim 1, wherein the predetermined priority criteria include at least one of a higher risk of wildfire in a depicted area and signals from previously detected smoke. ### Claim 7 (depends on 1) The system of claim 1, wherein the changes between consecutive image frames include at least one of direction of smoke changes, pattern of smoke changes, and shape of smoke changes. ### Claim 8 (depends on 1) The system of claim 1, wherein the score is outputted as a single numerical score or as a categorical score. ### Claim 9 (depends on 1) The system of claim 1, wherein the deep learning-based analysis module is further configured to evaluate a predicted wildfire smoke detection against an actual wildfire occurrence to assess an accuracy of the smoke detection model. ### Claim 10 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. ### Claim 11 (depends on 10) The method of claim 10, further comprising, in response to detecting smoke, controlling the imaging device to focus on an area where the smoke detection occurred. ### Claim 12 (canceled) ### Claim 13 (depends on 10) The method of claim 10, wherein the predetermined filtration criteria include at least one of clarity, format, and integrity of the collected image data. ### Claim 14 (canceled) ### Claim 15 (depends on 10) The method of claim 10, wherein the predetermined priority criteria include at least one of a higher risk of wildfire in a depicted area and signals from previously detected smoke. ### Claim 16 (depends on 10) The method of claim 10, wherein the changes between consecutive image frames include at least one of direction of smoke changes, pattern of smoke changes, and shape of smoke changes. ### Claim 17 (depends on 10) The method of claim 10, wherein the score is outputted as a single numerical score or as a categorical score. ### Claim 18 (depends on 10) The method of claim 10 further comprising evaluating a predicted wildfire smoke detection against an actual wildfire occurrence to assess an accuracy of a smoke detection model. ### Claim 19 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. ### Claim 20 (canceled) ### Claim 21 (canceled) ### Claim 22 (depends on 19) The device of claim 19, wherein the predetermined filtration criteria include at least one of clarity, format, and integrity of the collected image data. ### Claim 23 (canceled) ### Claim 24 (depends on 19) The device of claim 19, wherein the predetermined priority criteria include at least one of a higher risk of wildfire in a depicted area and signals from previously detected smoke. ### Claim 25 (depends on 19) The device of claim 19, wherein the changes between consecutive image frames include at least one of direction of smoke changes, pattern of smoke changes, and shape of smoke changes. ### Claim 26 (depends on 19) The device of claim 19, wherein the score is outputted as a single numerical score or as a categorical score. ### Claim 27 (depends on 19) The device of claim 19, wherein the device is further configured to evaluate a predicted wildfire smoke detection against an actual wildfire occurrence to assess an accuracy of a smoke detection model.