Welcome to
LeezencounterA demonstration of AI-powered bicycle monitoring for Leezenbox facilities. This system tracks occupancy patterns using computer vision technology to provide insights into urban bike-sharing usage.
What we do
This project develops a monitoring system for Leezenbox bicycle facilities using computer vision technology. The system automatically detects and counts bicycles to track occupancy levels, providing data that could help inform decisions about bike-sharing infrastructure usage patterns.
How it works
The system uses YOLO object detection models running on ESP32 microcontrollers to analyze images from Leezenbox locations. Data is processed locally and transmitted via LoRaWAN networks. This approach aims to balance detection accuracy with low power consumption and privacy considerations.
Who we are
We are master's students from the University of Münster working on this project as part of our studies in TinyAIoT applications. The goal is to explore how machine learning and IoT technologies can be applied to urban mobility challenges through a practical proof of concept.
Made with ❤️ in Münster