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

Mauricio Arias-Correa; Jorge Alexander David Rodríguez; Mateo Quintero Restrepo; Paula Alejandra Ortiz Santana; Luis Miguel Gómez Meneses

doi:10.24054/rcta.v1i43.2820

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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