Uma proposta para detecção de buracos com aprendizagem de máquina na borda

Abstract

Potholes in urban roads represent a significant problem, affecting both user safety and vehicle durability. This study addresses the urgent need for effective solutions for pothole detection that can be implemented in real-time using devices with limited computational resources. An innovative approach was developed, integrating machine learning on edge devices, with an emphasis on YOLOv8 and FOMO models within the TinyML context. A specialized dataset, containing annotated images, was used to train these models for accurate pothole detection. The optimization of YOLOv8 and FOMO models’ performance for edge devices ensures real-time efficiency. This work not only provides effectively trained models but also presents an adaptable framework for pothole detection, ensuring practical and efficient implementation. Additionally, a complete pipeline for pothole detection is proposed, validating the models’ accuracy and efficiency. This approach offers a robust solution for the automatic recognition of potholes, significantly contributing to improvements in urban infrastructure maintenance.