Evaluating pedestrian crossing safety: Implementing and evaluating a convolutional neural network model trained on paired aerial and subjective perspective images

Evaluating pedestrian crossing safety: Implementing and evaluating a convolutional neural network model trained on paired aerial and subjective perspective images

With pedestrian crossings implicated in a significant proportion of vehicle-pedestrian accidents and the French government's initiatives to improve pedestrian safety, there is a pressing need for efficient, large-scale evaluation of pedestrian crossings. This study proposes the deployment of ad...

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Bibliographic Details
Main Authors: Dylan Russon, Antoine Guennec, Juan Naredo-Turrado, Binbin Xu, Cédric Boussuge, Valérie Battaglia, Benoit Hiron, Emmanuel Lagarde
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Heliyon
Subjects:
Pedestrian safety
Pedestrian crossings
Deep learning
Convolutional neural networks
Image segmentation
Infrastructure analysis
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844025008084
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