Portable Target Evaluation Unit Using Machine Learning
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Solution Overview
Problem
Existing target evaluation systems in shooting ranges require additional equipment, external cameras, and modifications to firearms or facilities, making them cumbersome and less engaging for users, limiting opportunities for frequent practice and proficiency improvement.
Innovation Solution
A portable, self-contained target evaluation unit that uses machine learning to analyze images from a camera to identify target types, determine distances, detect hits, and score sessions without modifying firearms or facilities, allowing for real-time scoring with conventional paper targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external cameras and specialized equipment are used for target evaluation, then scoring accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines the camera, processing device, and storage into a single integrated portable target evaluation unit. This merging of components that were previously separate (external cameras, servers, specialized equipment) into one self-contained device reduces overall system complexity while maintaining scoring accuracy through integrated image capture, analysis, and evaluation functions.
Solution Approach 2:
The portable target evaluation unit is self-contained and autonomous, performing all target detection, hit identification, and scoring functions without requiring external servers or additional specialized equipment. The unit processes images locally using machine learning models stored within it, eliminating dependency on external infrastructure and reducing system complexity.
2Reliability
If modifications to firearms or facilities are made for target evaluation, then scoring reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent extracts the evaluation functionality from the firearm and facility infrastructure, placing it entirely in the portable evaluation unit. No modifications are required to the firearm, target, or facility - the unit independently captures images and performs all analysis, maintaining scoring reliability while preserving ease of operation by leaving existing equipment unchanged.
Solution Approach 2:
The portable evaluation unit is designed to work with conventional paper targets and standard firearms without modification. Its machine learning models can identify various target types and hit patterns universally, making the system adaptable to different shooting scenarios while requiring no changes to the firearm or facility infrastructure.
3Ease of manufacture
If conventional paper targets are used without modifications, then ease of manufacture is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces traditional mechanical or optical hit detection methods (which would require modified targets with sensors or special markings) with an image processing-based system. The machine learning model analyzes standard paper target images to identify hits with high precision, eliminating the need for mechanically modified targets while maintaining ease of manufacture.
Solution Approach 2:
The machine learning model is trained to recognize hit patterns on conventional paper targets by analyzing image parameters such as pixel intensity changes, edge detection, and pattern recognition. This allows precise hit detection on unmodified paper targets by transforming the detection approach from mechanical/sensor-based to image-analysis-based.
Data Source
AI summary
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.