A Comparative Analysis of Pedestrian Detection Performance for YOLOv8 Models on the BDD100K Dataset

Yaxiaer Yashengjiang

2025

Abstract

Efficient and reliable pedestrian detection is paramount for autonomous driving safety. This paper presents a systematic comparison of YOLOv8 models of varying scales (s, m, l) in complex driving scenarios, focusing on performance trade-offs. The models were trained and evaluated on the "pedestrian" class of the BDD100K dataset, covering core metrics like mean Average Precision (mAP), precision, recall, model parameters, and inference speed. Results show a positive correlation between model scale and detection performance. YOLOv8l achieved the highest accuracy (mAP50 of 0.683) and demonstrated superior robustness, especially in challenging nighttime, rainy conditions, and for small object detection. However, this came at the cost of the slowest inference speed. Conversely, YOLOv8s offered the fastest inference but compromised on accuracy. This quantitative analysis reveals the inherent trade-offs between accuracy, efficiency, and robustness among YOLOv8 models, providing empirical data to guide model selection for autonomous systems based on specific hardware and performance needs.

Paper Citation

in Harvard Style

Yashengjiang Y. (2025). A Comparative Analysis of Pedestrian Detection Performance for YOLOv8 Models on the BDD100K Dataset. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 222-226. DOI: 10.5220/0014325600004718

in Bibtex Style

@conference{emiti25,
author={Yaxiaer Yashengjiang},
title={A Comparative Analysis of Pedestrian Detection Performance for YOLOv8 Models on the BDD100K Dataset},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={222-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014325600004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - A Comparative Analysis of Pedestrian Detection Performance for YOLOv8 Models on the BDD100K Dataset
SN - 978-989-758-792-4
AU - Yashengjiang Y.
PY - 2025
SP - 222
EP - 226
DO - 10.5220/0014325600004718
PB - SciTePress