A computer vision application for automatic detection of defects in urban asphalt pavement in Brazil - Borges, Marcos Augusto; Spanhol, Fabio Alexandre (Anais do Workshop Latinoamericano de Dependabilidade e Segurança em Sistemas Veiculares (SSV); 2025: Anais do II Workshop Latinoamericano de Dependabilidade e Segurança em Sistemas Veiculares, 2025)
This work aims to develop an automated system for detecting damage in asphalt pavements by leveraging computer vision and deep learning techniques. It addresses key challenges such as the accurate identification and classification of various types of pavement distress. As a central contribution, we present a publicly available dataset of high-resolution digital images of urban pavements, structured in accordance with Brazilian standards and accompanied by per-image annotations curated by a technical committee. Based on this dataset, the study also presents initial experiments that implement a methodology for the automated diagnosis of road conditions. The model trained with YOLOv5 achieved an mAP@50 of 84.4%. The proposed approach is intended to support public administration in decision-making processes related to road maintenance and intervention planning, ultimately contributing to improvements in the quality, efficiency, and safety of urban road infrastructure.