PVNet Summer Research Project
2025 EXPLORATORY INTERNSHIP PROJECT
Bike & eBike Rider Detection and Alert System
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Objective:
To help students in grades 9–12 and college design and build a wireless, low-light-capable detection system that identifies people riding bicycles or e-bikes in restricted areas and sends real-time SMS and email alerts to security personnel.
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Project Overview:
Students will develop a working system using Raspberry Pi hardware, a high-resolution camera, and computer vision tools like YOLOv8. The device will deliver alerts through connected networks. Students will gain experience with AI, programming, electronics, and applied engineering in a real-world public safety application.
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Research Questions:
Can a small system accurately detect a person riding a bicycle in real time?
How can we reduce false positives while maintaining fast response alerts?
What are the best practices for safely installing surveillance hardware in public spaces?​
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Procedure:
Train a computer vision model to detect person+bike activity.
Assemble and configure the Raspberry Pi detection system.
Test and fine-tune the system in a controlled environment.
Deploy the system in a restricted zone for live testing.
Log, evaluate, and adjust performance based on real-world feedback.
Present results and demonstrate the working prototype.
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Expected Learning Outcomes:
Learn how to apply AI and computer vision to real-world safety problems.
Understand system integration across hardware, software, and networking.
Gain experience in project planning, testing, and technical reporting.
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We hope you'll join us in creating a safer community for everyone!