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

• Ano de defesa: 2024
• Autor(a) principal: Silva, Jordão Paulino Cassiano da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA E DE COMPUTAÇÃO
• Palavras-chave em Português: Aprendizado de máquina na borda; Infraestrutura urbana; Detecção de buracos; Otimização de modelos; TinyML; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufrn.br/handle/123456789/58993

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.