10 Years of Deep Learning for Vehicle Detection at a Smart Parking : What has Changed? - da Luz, Gustavo P. C. P; Sato, Gabriel Massuyoshi; Bannwart, Tiago Godoi; Gonzalez, Luis Fernando Gomez; Borin, Juliana Freitag (Anais do Workshop de Computação Urbana (CoUrb); 2025: Anais do IX Workshop de Computação Urbana, 2025)
Over the past decade, deep learning has transformed vehicle detection systems, particularly in smart parking applications. This work presents the evolution of a near real-time parking lot monitoring system at a university campus through multiple research and development iterations. The latest version incorporates the YOLOv11m model, converted to TensorFlow Lite, achieving a balanced accuracy of 98.65% on a dataset of 3,484 images, with an inference time of 8 seconds and updates sent to the parking totem every minute. The system integrates a Raspberry Pi 3 for edge inference, sending the number of available parking spaces to an InfluxDB database, while an ESP8266 receives this data and displays it on an LED panel at a totem. Earlier versions faced challenges such as hardware limitations, lack of documentation, and reliance on outdated deep learning models. The new version addresses these issues, improving detection accuracy, with enhanced fault tolerance, and a more robust totem structure. Additionally, a benchmark comparing 13 deep learning models for smart parking is introduced, along with software and hardware instructions for system replication.