MCU Bicycle Counting

Within this project, microcontroller units (MCUs) were utilized in combination with quantized object detection models.

The goal of this project was to enable the real-time on-device bicycle detection in public spaces and transmit the bicycle detections via LoRaWAN to a web application. The web application provides a detailed overview of the occupancy statistics where the corresponding hardware is deployed.

The repository is organized as a multi-component project (mono repository). See the repository structure for details.

The work was published as an experience paper at the ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems (SenSys '26). See details how to cite below.

Hardware

We used the XIAO ESP32-S3 Sense MCU with the XIAO Wio-SX1262 LoRa module. The default camera module of the ESP32-S3 was replaced by the OV5640 21mm 160 degrees one, which enabled the capturing of wider images and higher resolutions.

A custom 3D-printed case for the hardware was designed for mounting purposes.

Software

The following software stack was used:

Model Training

• Python >=3.10
• Ultralytics

Model Conversion & Deployment

• ESP-IDF v5.5 (C/C++ project compilation and on-device application)
• ESP-DL (on-device model inference)
• ESP-PPQ (model compression)
• Arduino

Sketches

• Arduino IDE

A snapshot of the images used for model training can be found in ./model-training/datasets.

Mono Repository Structure

.
├── model-training          // Experiments for model training and fine-tuning
│   ├── data                // training data
│   ├── model_training      // main directory containing experiment code
│   ├── models              // trained and loaded models
│   └── notebooks           // Jupyter notebooks for local experimentation
├── model-deployment        // Model deployment files for ESP32-S3
├── cad                     // CAD files for 3D-printable case
└── sketches                // Arduino Sketches for MCUs

Have a look at the sub-repository README files for more details.

Project Building

The project workflow was as follows (if you want to re-produce the results or re-use this project for your purposes):

1. sketches (camera capture scripts) - Collect images
2. model-training - Train/Fine-tune model utilizing the images collected from sketches scripts
3. model-deployment (model_conversion package) - Convert and compress the fine-tuned YOLO model
4. model-deployment (yolo11_detect) - Build the ESP-IDF project and deploy quantized model on hardware; send model predictions via BLE
5. sketches (lorawan_send package) - Receive model predictions via BLE and forward results via LoRaWAN to TNN node, which get fetched from the web application
6. (cad - Use/Customize 3D-printable case (e.g., with Tinkercad) to have an out-of-the-box usable mounting solution; optional)

Licence

This software is licensed under the GNU Affero General Public License v3.0.

This project makes use of Ultralytics, which is licensed under the same terms.

Cite

@inproceedings{stenkamp2026bikecount,
author = {Stenkamp, Jan and Hunke, Mathis and Karatas, Cem and Knaden, Christoph and Naebers, Paul and Zhao, Lige Karic, Benjamin and Gieseke, Fabian and Herrmann, Nina},
title = {Counting Parked Bicycles on the Edge - A TinyML Smart City Application},
year = {2026},
doi = {10.1145/3774906.3802788},
booktitle = {ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems},
series = {SenSys '26}
}

Contributing

Contributions are welcome! Please open issues or submit pull requests.