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The development of a prototype solution for detecting wear and tear in pedestrian crossings
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Orientador(es)
Resumo(s)
Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions.
Descrição
Palavras-chave
Pedestrian crossings Smart cities Computer vision Convolutional neural networks Performance evaluation
Contexto Educativo
Citação
ROSA, Gonçalo J.M. [et al.] (2024) - The development of a prototype solution for detecting wear and tear in pedestrian crossings. Applied Sciences. Vol. 14, n.º 15, p.6462. DOI: 10.3390/app14156462
Editora
MDPI