Peer-reviewedLicence not statedHeliyon
Evaluating Pedestrian Crossing Safety: Implementing and Evaluating a Convolutional Neural Network Model Trained on Paired Aerial and Subjective Perspective Images (Vol 11, E42428, 2025)
Related
- Evaluating Autonomous LLM Agents Across Molecular Prediction and Optimization Benchmarks
- BioTrajX: evaluating biological plausibility in single-cell trajectory inference
- Beyond compliance: evaluating the impact of assent-based training on equine behavior and the human-horse relationship.
- Evaluating Bank Restoration and Stabilization in the Yellow River in Northern Indiana: Mitigation of Sediment and Nutrient Transport
- Evaluating aldicarb and fluazaindolizine for management of Meloidogyne incognita in Arkansas soybean production
BibTeX
@article{russon2026evaluating,
title = {{Evaluating Pedestrian Crossing Safety: Implementing and Evaluating a Convolutional Neural Network Model Trained on Paired Aerial and Subjective Perspective Images (Vol 11, E42428, 2025)}},
author = {Dylan Russon and Antoine Guennec and Juan Naredo-Turrado and Binbin Xu and Cedric Boussuge and Valerie Battaglia and Benoit Hiron and Emmanuel Lagarde},
journal = {Heliyon},
year = {2026},
month = oct,
doi = {10.1016/j.heliyon.2025.e43902},
url = {https://hal.science/hal-05786927v1},
}
RIS
TY - JOUR
TI - Evaluating Pedestrian Crossing Safety: Implementing and Evaluating a Convolutional Neural Network Model Trained on Paired Aerial and Subjective Perspective Images (Vol 11, E42428, 2025)
AU - Dylan Russon
AU - Antoine Guennec
AU - Juan Naredo-Turrado
AU - Binbin Xu
AU - Cedric Boussuge
AU - Valerie Battaglia
AU - Benoit Hiron
AU - Emmanuel Lagarde
PY - 2026
DA - 2026/10/08
JO - Heliyon
DO - 10.1016/j.heliyon.2025.e43902
UR - https://hal.science/hal-05786927v1
SN - 2405-8440
ER -