TinyML-Empowered Line Following for a Car Robot
Evaluating the Capabilities of Various Lane Detection Models on Microcontrollers
A.J.A. Carton de Wiart (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Q. Wang – Mentor (TU Delft - Embedded Systems)
R. Zhu – Mentor (TU Delft - Embedded Systems)
Rangarao Venkatesha Prasad – Graduation committee member (TU Delft - Networked Systems)
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Abstract
This research explores the feasibility of implementing lane detection on lightweight microcontrollers using a combination of traditional image processing and compact machine learning methods. With the aim of enabling real-time inference under strict hardware constraints, several models were trained and evaluated against a custom image processing pipeline. Each approach was tested for accuracy, speed, and resource usage on the Raspberry Pi Pico 0 microcontroller. While these solutions fall short of cutting-edge accuracy and cannot process as much information as state of the art models, their low cost, minimal power consumption, and real-time performance highlight their potential. These findings suggest that lightweight lane detection is a viable direction for further research in embedded autonomous systems.