verified Patent Pending · PAT 63/923,797

Coolsquid Defense Product

Vector

Range

Real time projectile impact detection and scoring using computer vision. Know where every round landsinstantly.

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System Active · 0 FPS

YOLOv8 · Dual Model Architecture

iOS · Android · Dedicated Hardware

The Problem

Fire. Wait. Walk. Repeat.

Standard range protocol: fire at a target, wait for the range officer to call the line cold, walk downrange to inspect your target, then try to remember what you felt when the shot broke. By the time you see the result, the feedback loop is broken.

Is the shooter anticipating recoil? Pulling the trigger too fast? Does the optic need adjustment? These questions can't be answered from memory. They need to be answered in real time, at the moment of the shot.

Existing Approach

Manual inspection. Broken feedback loop. Slow iteration. No performance data. High range time waste.

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Vector Range

Instant impact location. AI driven improvement feedback. No range cold required. Works with any target. No wires to shoot off.

Deployment Scenarios

military_tech Military & Training

Range Training & Qualification

Service members receive immediate AI driven feedback during drillswithout waiting for range cold, without manual intervention from a range officer. Performance data is captured in real time, enabling faster skill development and objective qualification scoring.

chevron_right Range officers can monitor multiple shooters simultaneously from a single dashboard
chevron_right Qual tests scored digitallyrange officer verifies, not manually tallies
chevron_right AI identifies technique issues: recoil anticipation, trigger pull timing, grouping drift
chevron_right Multi target detection from a single wide-FOV camera

Hardware Setup

videocam

~4 cameras mounted above the range, looking down at targets. Each shooting lane gets an iPad for the shooter's live view. Simple retrofit. No target modification required.

phone_iphone Civilian & Recreational

Smartphone App

Available to any shooter, at any range, with any target. Mount your phone to a spotting scope, open the app, and start shooting. Vector Range tracks every impact in real time, shows grouping data, and gives actionable coachingall without replacing anything you already own.

chevron_right Works with any standard targetno proprietary hardware required
chevron_right Tested at 10 to 100 yards across a wide range of calibers
chevron_right Real time grouping analysis and shot by shot history
chevron_right Native iOS and Androidpotential OEM bundling with optic manufacturers

Supported Calibers

.22LR .22MAG .223 9MM .357 12GA Slug 7.62x54r

How It Works

// YOLOv8 Dual Model Architecture

Stage One

Target Detection

The first YOLOv8 model locates and frames every target in the camera's field of view. Trained on ~1,000 images, it achieves 80%+ real-world accuracy across varying backgrounds, lighting conditions, and target types. When multiple targets are presentas in military range configurationsall are detected and tracked independently.

Each detected target establishes a coordinate space for impact calculations: distance from center, scoring ring intersection, and multi target grouping comparison.

Model Stats

80%+

Real-World Accuracy

~1,000

Training Images

Any

Standard Target Type

Target detection model — bounding box annotation

FIG. 2 — Target detection training sample

Stage Two

Bullet Impact Detection

The second model identifies and localizes individual bullet impacts. Trained on ~5,000 targetsincluding dirty splatter targets captured at outdoor ranges from 10 to 100 yards across multiple spotting scopes and weather conditionsit achieves ~70% accuracy across the supported caliber set.

Data augmentation during training ensures reliable performance on inputs that are scratched, off center, tilted, or captured under inconsistent zoom levels. The model handles real-world range conditions, not just clean lab data.

Model Stats

~70%

Impact Detection Accuracy

~5,000

Training Images

10–100

Yards Tested Range

Bullet impact detection — annotated training sample

FIG. 3 — Impact detection training sample

FIG. 5 — Training performance metrics

Stage Three

Combined Real Time Output

Both models run in parallel to deliver a unified real time overlay: current impact location, last known impact marker, running grouping size, and session historyall rendered at 30 FPS. Tested on iPhone 13 mini and iPhone Air, the system runs on consumer hardware with no external compute required.

The civilian app deploys via iOS and Android. The military system pairs with dedicated edge hardware for air gapped, low-latency range environments.

Performance

30 FPS

Real Time Processing

Tested Devices

iPhone 13 mini

iPhone Air

Deployment

iOS · Android · Edge Hardware

Why Vector Range Wins

Feature	Vector Range	ShotMarker	USMC Smart Target
Any target type	check	close	close
No wires	check	close	close
No shootable components	check	close	close
AI improvement coaching	check	close	close
Grouping analytics	check	close	close
Multi target from single camera	check	close	close
Low installation cost	check	close	close
Consumer smartphone app	check	close	close