Machine learning system for storage vessel fill level detection

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.

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.