Machine learning system for storage vessel fill level detection
Various embodiments of the present technology relate to systems and methods to determine fill levels in a fuel extraction and storage environment. In some examples, a system comprises a thermal imaging device, a machine learning interface, and a machine learning engine. The thermal imaging device generates a thermal image that depicts fuel storage equipment. The machine learning interface generates feature vectors based on the thermal image that depicts the fuel storage equipment and feeds the feature vectors to a machine learning engine. The machine learning engine ingests the feature vectors, generates a machine learning output that indicates a fill level for the fuel storage equipment based on the feature vectors, and transfers the machine learning output.
1 . A method of operating a detection system to determine fill levels in a fuel extraction and storage environment, the method comprising:
generating feature vectors based on a thermal image that depicts a fuel storage equipment;
feeding the feature vectors to a machine learning engine that comprises an object detection machine learning model, a fill level detection machine learning model, a shadow/reflection machine learning model, and a non-linear function machine learning algorithm;
receiving an object detection output from the object detection machine learning model that identifies portions of the thermal image that depict the fuel storage equipment;
receiving a fill level output from the fill level detection machine learning model that identifies portions of the thermal image that depict the fill level for the fuel storage equipment;
receiving a shadow/reflection output from the shadow/reflection machine learning model that identifies portions of the thermal image that depict shadows and/or reflections;
feeding the object detection output, the fill level output, and the shadow/reflection output to the non-linear function machine learning algorithm;
receiving a non-linear function output that indicates a fill percentage and a fill height for the fuel storage equipment; and
generating and transferring a notification based on the non-linear function output.
2 . The method of claim 1 wherein:
generating the feature vectors comprises generating numerical representations of the thermal image; and
feeding the feature vectors to the machine learning system comprises feeding the numerical representations of the thermal image to the machine learning engine.
3 . The method of claim 1 wherein feeding the object detection output, the fill level output, and the shadow/reflection output to the non-linear function machine learning algorithm comprises:
determining a fill level pixel height;
determining a total pixel height of the fuel storage equipment;
calculating a ratio of the fill level pixel height and the total pixel height to determine the fill percentage;
applying the fill level percentage to an actual fuel storage equipment volume and an actual fuel storage equipment height to determine an actual fill height, an actual fuel volume, and an actual unfilled volume; and
generating the non-linear function output that comprises the thermal image, the fill level indication, the fill percentage, the actual fill height, the actual fuel volume, and the actual unfilled volume of the fuel storage equipment.
4 . The method of claim 1 wherein feeding the object detection output, the fill level output, and the shadow/reflection output to the non-linear function machine learning algorithm comprises comparing the portions of the thermal image that depict shadows and/or reflections with the portions of the thermal image that depict the fill level for the fuel storage equipment to screen for false positive outputs.
5 . The method of claim 1 further comprising generating the thermal image that depicts a fuel storage equipment.
6 . The method of claim 1 wherein the notification comprises a command to fill the fuel storage equipment.
7 . The method of claim 1 wherein the notification comprises a command to not fill the fuel storage equipment.
8 . A detection system to determine fill levels in a fuel extraction and storage environment, the detection system comprising:
a thermal imaging device configured to generate a thermal image that depicts a fuel storage equipment;
a machine learning interface configured to generate feature vectors based on the thermal image that depicts the fuel storage equipment; and
the machine learning engine configured to ingest the feature vectors, wherein the machine learning engine comprises an object detection machine learning model, a fill level detection machine learning model, a shadow/reflection machine learning model, and a non-linear function machine learning algorithm;
the object detection machine learning model configured to generate an object detection output that identifies portions of the thermal image that depict the fuel storage equipment based on the feature vectors;
the fill level detection machine learning model configured to generate a fill level output that identifies portions of the thermal image that depict the fill level for the fuel storage equipment based on the feature vectors;
the shadow/reflection machine learning model configured to generate a shadow/reflection output from that identifies portions of the thermal image that depict shadows and/or reflections based on the feature vectors;
the non-linear function machine learning algorithm configured to determine a fill percentage and a fill height for of the fuel storage equipment based on the object detection output, the fill level output, and the shadow/reflection output;
the machine learning engine configured to generate and transfer a notification based on the non-linear function output.
9 . The detection system of claim 8 wherein:
the machine learning interface is to generate numerical representations of the thermal image to create the feature vectors and feed the numerical representations of the thermal image to the machine learning engine.
10 . The detection system of claim 8 wherein the non-linear function machine learning algorithm is further configured to:
determine a fill level pixel height;
determine a total pixel height of the fuel storage equipment;
calculate a ratio of the fill level pixel height and the total pixel height to determine the fill percentage;
apply the fill level percentage to an actual fuel storage equipment volume and an actual fuel storage equipment height to determine an actual fill height, an actual fuel volume, and an actual unfilled volume; and
generate the non-linear function output that comprises the thermal image, the fill level indication, the fill percentage, the actual fill height, the actual fuel volume, and the actual unfilled volume of the fuel storage equipment.
11 . The detection system of claim 8 wherein the non-linear function machine learning algorithm is further configured to compare the portions of the thermal image that depict shadows and/or reflections with the portions of the thermal image that depict the fill level for the fuel storage equipment to screen for false positive outputs.
12 . The detection system of claim 8 wherein the thermal imaging device is to film the fuel storage equipment to generate the thermal image.
13 . The detection system of claim 8 further comprising a user device to receive the machine learning output and transfer a command to fill the fuel storage equipment based on the machine learning output.
14 . The detection system of claim 8 further comprising a user device to receive the machine learning output and transfer a command to not fill the fuel storage equipment based on the machine learning output.
15 . A non-transitory computer-readable medium stored thereon program instructions to determine fill levels in a fuel extraction and storage environment, that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
generating feature vectors based on a thermal image that depicts a fuel storage equipment;
feeding the feature vectors to a machine learning engine that comprises an object detection machine learning model, a fill level detection machine learning model, a shadow/reflection machine learning model, and a non-linear function machine learning algorithm;
receiving an object detection output from the object detection machine learning model that identifies portions of the thermal image that depict the fuel storage equipment;
receiving a fill level output from the fill level detection machine learning model that identifies portions of the thermal image that depict the fill level for the fuel storage equipment;
receiving a shadow/reflection output from the shadow/reflection machine learning model that identifies portions of the thermal image that depict shadows and/or reflections;
feeding the object detection output, the fill level output, and the shadow/reflection output to the non-linear function machine learning algorithm;
receiving a non-linear function output that indicates a fill percentage and a fill height for the fuel storage equipment; and
generating and transferring a notification based on the non-linear function output.
16 . The non-transitory computer readable medium of claim 15 wherein:
generating the feature vectors comprises generating numerical representations of the thermal image; and
feeding the feature vectors to the machine learning system comprises feeding the numerical representations of the thermal image to the machine learning engine.
17 . The non-transitory computer readable medium of claim 15 wherein feeding the object detection output, the fill level output, and the shadow/reflection output to the non-linear function machine learning algorithm comprises:
determining a fill level pixel height;
determining a total pixel height of the fuel storage equipment;
calculating a ratio of the fill level pixel height and the total pixel height to determine the fill percentage;
applying the fill level percentage to an actual fuel storage equipment volume and an actual fuel storage equipment height to determine an actual fill height, an actual fuel volume, and an actual unfilled volume; and
generating the non-linear function output that comprises the thermal image, the fill level indication, the fill percentage, the actual fill height, the actual fuel volume, and the actual unfilled volume of the fuel storage equipment.
18 . The non-transitory computer readable medium of claim 15 wherein feeding the object detection output, the fill level output, and the shadow/reflection output to the non-linear function machine learning algorithm comprises comparing the portions of the thermal image that depict shadows and/or reflections with the portions of the thermal image that depict the fill level for the fuel storage equipment to screen for false positive outputs.
19 . The non-transitory computer readable medium of claim 15 wherein the notification comprises a command to fill the fuel storage equipment.
20 . The non-transitory computer readable medium of claim 15 wherein the notification comprises a command to not fill the fuel storage equipment.