Target Practice Evaluation Unit

Abstract

A method for evaluating hits on a target is disclosed comprising capturing frames of the target by a camera, detecting a target in a captured frame, classifying the target in the captured frame as a target type, determining a depth of the target from a user, identifying a hit on the target, by a processing device, and scoring the hit. Detecting the target, classifying the target, and/or identifying a hit on the target by a respective machine learning model run. A portable and self-contained target evaluation unit is also disclosed.

What is claimed is: 
     
         1 . A method for evaluating hits on a target, comprising:
 capturing frames of the target by a camera;   detecting a target in a captured frame, by a processing device;   classifying the target in the captured frame as a target type, by a processing device;   determining a depth of the target from a user;   identifying a hit on the target, by a processing device; and   scoring the hit, by a processing device.   
     
     
         2 . The method of  claim 1 , comprising:
 detecting the target, classifying the target, and/or identifying a hit on the target by a respective machine learning model run by a processing device.   
     
     
         3 . The method of  claim 2 , comprising:
 classifying the target as a known target by a machine learning model trained on known targets, the known target having known target area data stored in a storage device;   the method further comprising:   retrieving target area data stored in the storage device, by the processing device; and   scoring a hit on the target based, at least in part, on the target area data.   
     
     
         4 . The method of  claim 3 , wherein the target area data for a target includes a valid hit area, the method comprising:
 scoring the hit on the target based, at least in part, on whether the hit is within the valid hit area for the target.   
     
     
         5 . The method of  claim 4 , comprising scoring the hit based, at least in part, on a distance between the hit and a center of mass of a valid target hit area. 
     
     
         6 . The method of  claim 4 , wherein the target area data includes an invalid hit area, the method comprising:
 scoring the hit on the target based, at least in part, on whether the hit is within the valid hit area for the target.   
     
     
         7 . The method of  claim 3 , comprising determining whether the target is a known target by a machine learning model trained on a training set including known targets. 
     
     
         8 . The method of  claim 7 , wherein the training set includes synthetically generated targets based on actual targets, with a modified texture. 
     
     
         9 . The method of  claim 3 , wherein the known target type is a bullseye target type, a silhouette target type, or a hostage target type. 
     
     
         10 . The method of  claim 3 , wherein, if a known target is not identified based on the machine learning model, the method further comprises:
 running a second machine learning model different from the first machine learning model, the second machine learning model including multiple, different machine learning models for identifying different characteristics of the target; and   resolving the outputs of the multiple, different machine learning models to define target area data for the target.   
     
     
         11 . The method of  claim 10 , wherein the multiple machine learning models comprise a segmentation machine learning model, a circle detection machine learning model, a face detection machine learning model, and/or a sentiment detection machine learning model. 
     
     
         12 . The method of  claim 11 , comprising running the sentiment detection machine learning model only if at least one face is identified by the face detection machine learning model. 
     
     
         13 . The method of  claim 12 , wherein the sentiment detection machine learning model is trained on a training set including faces expressing anger, disgust, fear, sadness and/or surprise. 
     
     
         14 . The method of  claim 10 , wherein the target area data includes a valid hit area, the method comprising:
 scoring the hit on the target based, at least in part, on whether the hit is within the valid hit area for the target.   
     
     
         15 . The method of  claim 14 , wherein the target area data for a target includes an invalid hit area, the method comprising:
 scoring the hit on the target based, at least in part, on whether the hit is within the invalid hit area for the target.   
     
     
         16 . The method of  claim 10 , comprising scoring the hit based, at least in part, on a center of mass of an invalid target hit area. 
     
     
         17 . A system for evaluating bullet hits on a target, comprising:
 a camera to capture frames of a target;   at least one processing device; and   storage;   the at least one processing device configured to:
 detect a target in a captured frame; 
 classify the target in the captured frame as a target type; 
 determine a depth of the target from a user; 
 identify a hit on the target, by running a machine learning model; and 
 score the hit. 
   
     
     
         18 . The system of  claim 17 , further comprising:
 a casing having an opening, wherein the camera has a camera lens proximate the opening to capture frames down range of the casing;   wherein the camera, the processing device, and the storage are contained within a portable casing; and   the system is self-contained and portable.   
     
     
         19 . The system of  claim 16 , wherein the casing has a second opening different from the first opening, and contains a second camera different from the first camera, the second opening and the second camera being configured to image at least a user's shooting hand during use. 
     
     
         20 . The system of  claim 17 , wherein the at least one processing device is configured to:
 classify the target in the captured frame as a target type by running a machine learning model.   
     
     
         21 . A method for evaluating bullet hits on a target, comprising:
 classifying the target in the captured frame as a target type by running a first machine learning model;   identifying a hit on the target by running a second machine learning model different from the first machine learning model; and   scoring the hit.   
     
     
         22 . The method of  claim 20 , further comprising:
 determining a depth of the target from a user by a third machine learning model different from the first and second machine learning model.