Counting Parked Bicycles on the Edge - A TinyML Smart City Application
Abstract
As cities strive to reduce car dependency and promote sustainable transportation, encouraging bicycle usage becomes a vital part of the urban planning process. The existence of a sufficient number of bicycle storage facilities is a key building block, as it reduces the likelihood of bicycle theft and the necessity for bicycle repairs. By monitoring the utilization of bicycle parking lots, supply shortfalls can be detected, and users can be informed about the availability of slots. However, detection systems face multiple challenges. Equipping every parking slot with individual sensors is costly, and transmitting visual data can raise privacy concerns or even discourage users. To address this problem, embedded machine learning can be used to process visual data locally and transmit only the resulting count to a central server. This work sets out a real-world use case for microcontrollers that are equipped with a camera and an embedded machine learning model for the purpose of counting parked bicycles. A custom dataset was collected and labeled to train an object-detection model, which was subsequently compressed and deployed on an ESP32-S3 microcontroller that processes the image data locally and transmits only the bicycle count to a remote server via LoRaWAN. The model compression incurs only a marginal performance degradation, with the compressed model still achieving an AP@50 of 0.91. Hence, our approach demonstrates the practical realization of recent theoretical advances in tiny machine learning and provides a viable solution for monitoring bicycle parking facilities in real-world settings.
Keywords
Tiny Machine Learning, Sensor Data, Object Detection, Bicycle Monitoring, Real-World Datasets, Smart Cities