Un modelo de clasificación del estado del pavimento a partir de imágenes, tomas video y señales de GNSS

Abstract

Mobile mapping systems are widely used for urban observation. By integrating electronic devices with various sensors (including optical and thermal), computer vision techniques, AI algorithms, and GNSS positioning systems, data from urban road corridors can be collected. This research presents a review of conventional mobile mapping methods and proposes an advanced computer vision approach that leverages AI to improve mapping accuracy. The limitations and challenges of traditional methods, such as vehicle-mounted LIDAR systems, are critically examined. The study highlights how AI integration, particularly deep learning algorithms, has significantly enhanced data interpretation and decision-making processes in road infrastructure management. An experiment was conducted on Bogotá's road network using a mobile mapping system equipped with video sensors and GNSS devices in a specially adapted vehicle. This setup facilitated the collection of large datasets, which were subsequently processed using the YOLO AI algorithm to effectively detect and classify road pavement conditions. The experiment's results underscore the effectiveness of combining mobile mapping technology, programming, and AI algorithms to create detailed maps and identify key objects in the urban environment. Practical applications of this technology are discussed, focusing on creating detailed urban maps under the technical guidelines of responsible road agencies. Additionally, the identification of traffic congestion zones and the evaluation of the road network's condition are addressed. The use of these integrated hardware and software systems provides a fast and cost-effective solution for studying the state of both urban and rural road corridors.