Sponsor & Funding

Smart Basketball Court System

To ensure fair foul judgments in friendly basketball games, I’m building a system that uses sensors and AI to detect fouls objectively and in real-time.

1. Smart Basketball Equipment

(1) Wearable Devices

Lightweight wristbands, knee pads, or shoe sensors equipped with accelerometers, gyroscopes, and pressure sensors to capture movement, collision intensity, stepping out of bounds, and illegal steps.

(2) Smart Basketball

Built-in sensors track ball speed, position, and contact details, helping detect illegal hand-checking or slapping.

(3) Court Sensors

Pressure sensors under the floor track player positions. A multi-camera system supports foul detection using computer vision.

2. AI Analysis

• Action Recognition: Analyze player movement patterns.
• Collision Detection: Identify fouls based on impact intensity.
• Rule Matching: Compare sensor data to a rule database and make real-time decisions.

3. Real-Time Feedback

• Foul notifications on screen or mobile.
• Optional slow-motion replay to explain decisions.

4. Benefits & Challenges

Benefits: More objective gameplay, fewer disputes, and a fun tech-enabled experience.
Challenges: Device cost, ensuring data accuracy, and tuning rule thresholds.

This system can benefit casual players, amateur leagues, and training programs. Funding supports prototype development, testing, and scaling.

🚤 AI-Powered Underwater Fish Detection

Many anglers spend hours searching for fish. I’m building an AI-powered underwater detection tool to help fishers locate promising spots more easily and affordably.

The Vision

A portable underwater imaging device that lets fishers:

• View real-time underwater activity
• Detect and count fish automatically
• Estimate fish density
• Identify good fishing spots quickly

How It Works

1. An underwater camera attaches to a boat and streams live video.
2. AI models detect fish, count them, and estimate density.
3. When density is high enough, the system alerts the user.
4. Optimized for edge devices (Raspberry Pi, Jetson Nano).

Why It Matters

• Affordable alternative to commercial fish finders
• Open source and customizable
• Useful for recreational fishers, researchers, and conservation groups
• Great showcase of computer vision + edge AI

What Sponsorship Supports

• Underwater dataset collection
• Model training (YOLO/RT-DETR)
• Hardware prototyping (camera, housing, mini-computer)
• Field testing in lakes and coastal areas
• Documentation, tutorials, and open-source releases

If you love fishing, AI, or practical open-source tools, consider supporting this project and helping create a smarter way to fish.