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YOLO architectures comparison for urban cyclist detection in an autonomous driving environment

Authors

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

DOI:

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

Keywords:

Yolo, VRU, deep learning, cyclists' detection, autonomous vehicle

Abstract

The World Health Organization (WHO) states that over 55% of road traffic accident fatalities involve vulnerable road users, including 3% who are cyclists. While autonomous vehicles are capable of detecting objects and individuals on roadways, the detection of cyclists and the prediction of their movements continue to pose significant challenges. This paper presents results from the comparison of YOLOv7, YOLOv8, and YOLO-NAS architectures for urban cyclist detection. The methodology ensures that the detectors were trained under the same conditions. Subsequently, they were evaluated using 111 cyclist images with metrics such as IoU, precision, and recall. The results highlight advantages and disadvantages within each architecture, suggesting a priority for either inference time or the quality of cyclist detection in future work.

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Published

2024-03-13

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How to Cite

[1]

J. A. David Rodríguez, P. A. Ortiz Santana, M. Quintero Restrepo, L. M. Gómez Meneses, and M. Arias-Correa, “YOLO architectures comparison for urban cyclist detection in an autonomous driving environment”, RCTA, vol. 1, no. 43, pp. 64–72, Mar. 2024.