Firearm Training System with Barcode Target Detection
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Solution Overview
Problem
Firearm training systems lack real-time feedback and modularity, requiring significant infrastructural planning and manual inspection to evaluate user performance.
Innovation Solution
A system comprising an image sensor, processing subsystem, and control subsystem that captures and analyzes images of a target to determine projectile strikes, using barcodes for spatial information and operating in multiple modes to provide real-time feedback and adjust imaging parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional paper or plastic bullseye targets are used with manual inspection, then the system is simple and requires minimal infrastructure, but real-time feedback is lost and user performance evaluation is delayed
Solution Approach 1:
The patent implements real-time feedback by using an image sensor to capture images of the target after projectile discharge and automatically analyzing the images to determine strike accuracy. The system provides immediate performance evaluation to the user, eliminating the delay of manual inspection while maintaining relative system simplicity through automated image processing algorithms.
Solution Approach 2:
The patent replaces the mechanical manual inspection process with an automated optical system. An image sensor captures images of the target, and computer vision algorithms automatically analyze the images to detect projectile strikes and calculate accuracy metrics, substituting human manual evaluation with automated optical-electronic processing.
2Reliability
If laser-based firearms with virtual training scenarios are used, then training efficacy is improved, but modularity is reduced and significant infrastructural planning is required
Solution Approach 1:
The patent creates a universal training system that can work with multiple types of firearms (traditional firearms, laser-based firearms, training guns) by using an image sensor-based detection method rather than firearm-specific technology. The system can detect both physical projectile impacts and laser strikes on targets, providing multi-functionality and adaptability across different training scenarios without requiring firearm-specific infrastructure.
Solution Approach 2:
The patent introduces a target with machine-readable code as an intermediary between the firearm and the detection system. The target serves as a universal interface that can receive various types of projectiles or laser strikes and translate them into detectable signals for the image sensor, enabling modularity and interchangeability of different firearm types while maintaining consistent training evaluation.
3Productivity
If manual inspection of bullseye targets is used, then device complexity is minimized, but productivity and training efficiency are reduced due to delayed performance evaluation
Solution Approach 1:
The patent implements self-service evaluation where the system automatically captures images of the target, analyzes the strike patterns, and generates performance metrics without requiring instructor intervention. The automated image analysis algorithm independently evaluates training performance, providing immediate feedback that enhances training efficiency while keeping the system relatively simple through algorithmic self-assessment.
Solution Approach 2:
The patent enables continuous training evaluation by automatically capturing and analyzing images after each projectile discharge or laser strike. The system continuously monitors training performance without interruption, providing real-time feedback that maintains training momentum and efficiency, unlike manual inspection which creates interruptions and delays in the training process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time assessment of firearm training performance, enhances modularity, and reduces infrastructural requirements by providing immediate feedback and statistical data on accuracy.
Implementation Method 1
an image sensor having a lens defining a field of view of a scene that includes the target
Data Source
AI summary
A system trains usage of a firearm and includes an end unit, a processing subsystem, and a control subsystem remotely located from the end unit. The end unit includes an image sensor that is positioned against a target that has a bar code. The image sensor defines a field of view of a scene that includes the target, and the bar code stores encoded information that defines a target coverage zone. The system selectively operates in a first mode and a second mode according to input from the control subsystem. In the first mode the end unit scans the bar code to extract the target coverage zone. In the second mode the image sensor captures a series of images of the target coverage zone, and the processing subsystem analyzes regions of the captured series of images to determine a strike, by a projectile of the firearm, on the target.