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Comparación de arquitecturas YOLO para la detección de ciclistas urbanos en un entorno de vehículos autónomos

Autores/as

Mauricio Arias-Correa Instituto Tecnológico Metropolitano https://orcid.org/0000-0003-0619-235X
Jorge Alexander David Rodríguez Instituto Tecnológico Metropolitano https://orcid.org/0009-0002-6718-9955
Mateo Quintero Restrepo Instituto Tecnológico Metropolitano https://orcid.org/0009-0002-3138-4409
Paula Alejandra Ortiz Santana Instituto Tecnológico Metropolitano https://orcid.org/0009-0004-0480-7423
Luis Miguel Gómez Meneses Instituto Tecnológico Metropolitano https://orcid.org/0000-0002-0667-7472

DOI:

https://doi.org/10.24054/rcta.v1i43.2820

Palabras clave:

Yolo, VRU, deep learning, detección de ciclistas, vehículo autónomo

Resumen

La OMS establece que más del 55% de las muertes en accidentes viales son de usuarios vulnerables, incluyendo un 3% de ciclistas. Aunque los vehículos autónomos pueden detectar objetos y personas en las carreteras, la detección de ciclistas y la predicción de sus movimientos siguen siendo desafíos significativos. Este artículo presenta resultados al comparar las arquitecturas YOLOv7, YOLOv8 y YOLO-NAS para detectar ciclistas urbanos. La metodología garantiza que los detectores se entrenaron bajo las mismas condiciones. Luego, se evaluaron con 111 imágenes de ciclistas utilizando métricas como IoU, precisión y recall. Los resultados destacan ventajas y desventajas en cada arquitectura, lo que sugiere priorizar el tiempo de inferencia o la calidad de la detección de ciclistas en futuros trabajos.

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Publicado

2024-03-13 — Actualizado el 2024-03-13

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Cómo citar

Arias-Correa, M., David Rodríguez, J. A., Quintero Restrepo, M., Ortiz Santana, P. A., & Gómez Meneses, L. M. (2024). Comparación de arquitecturas YOLO para la detección de ciclistas urbanos en un entorno de vehículos autónomos. REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA), 1(43), 64–72. https://doi.org/10.24054/rcta.v1i43.2820