US 11,875,301 B2
Refuse contamination analysis
Anthony Romano, Havertown, PA (US); Michael Dean Geary, Toronto (CA); John Forrest Smith, Flat Rock, AL (US); and Garrett Eckerl, Fort Payne, AL (US)
Assigned to The Heil Co., Chattanooga, TN (US)
Filed by the Heil Co., Chattanooga, TN (US)
Filed on Jul. 26, 2019, as Appl. No. 16/523,903.
Claims priority of provisional application 62/760,764, filed on Nov. 13, 2018.
Claims priority of provisional application 62/711,367, filed on Jul. 27, 2018.
Prior Publication US 2020/0034785 A1, Jan. 30, 2020
Int. Cl. G06Q 10/0832 (2023.01); B65F 1/00 (2006.01); G06N 20/00 (2019.01); G06V 20/20 (2022.01); G06F 18/24 (2023.01); G06V 20/00 (2022.01)
CPC G06Q 10/0832 (2013.01) [B65F 1/0033 (2013.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01); G06V 20/00 (2022.01); G06V 20/20 (2022.01); B65F 2001/008 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for analyzing refuse, the method comprising:
receiving, by one or more processors, a plurality of images of refuse, each image of the plurality of images being labeled as either including contaminated refuse or not including contaminated refuse;
training, by the one or more processors and based on the labeling, at least one machine learning model using the plurality of images; and
receiving, by at least one processor, sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV), the sensor data generated by a sensor device configured to detect the operational state of the vehicle body component;
detecting, by the at least one processor, a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data;
in response to detecting the triggering condition, accessing, by the at least one processor, image data indicating a physical state of refuse collected by the RCV, the image data being generated by a camera mounted on the RCV and configured to generate digitized images of the refuse;
providing, by the at least one processor, the image data as input to the trained at least one machine learning model, to output a classification of the image data, wherein the classification indicates a degree of contamination of the refuse;
storing, by the at least one processor in a machine-readable medium, the classification of the image data;
receiving, by the at least one processor from a user, feedback regarding the accuracy of the classification of the image data output by at least one the machine learning model; and
retraining, by the at least one processor, the at least one machine learning model based on the image data, the classification, and the feedback regarding the accuracy of the classification.