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Publication
The development of a prototype solution for detecting wear and tear in pedestrian crossings
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| 14.11 MB | Adobe PDF |
Advisor(s)
Abstract(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.
Description
Keywords
Pedestrian crossings Smart cities Computer vision Convolutional neural networks Performance evaluation
Citation
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
Publisher
MDPI