Class-Specific Dataset Splitting for YOLOv8: Improving Real-Time Performance in NVIDIA Jetson Nano for Faster Autonomous Forklifts

Chaouki Tadjine, Chaouki Tadjine, Abdelkrim Ouafi, Abdelmalik Taleb-Ahmed, Yassin El Hillali

2025

Abstract

This research examines a class-specific YOLOv8 model setup for real-time object detection using the Logistics Objects in Context dataset, specifically looking at how it can be used in high-speed autonomous forklifts to enhance obstacle detection. The dataset contains five common object classes in logistics warehouses. It is divided into transporting tools (forklift and pallet truck) and goods-carrying tools (pallet, small load carrier, and stillage) to meet specific task needs. Two YOLOv8 models were individually trained and implemented on the NVIDIA Jetson Nano, with each one specifically optimized for a tool category. Using this approach tailored to specific classes resulted in a 30.6 percent decrease in inference time compared to training a single YOLOv8 model on all classes. Task-specific detection saw a 74.4 percent improvement in inference time for transporting tools and 56.2 percent improvement for goods-carrying tools. Furthermore, the technique decreased the hypothetical distance traveled during inference from 45.14 cm to 31.32 cm and even as low as 11.55 cm for transporting tools detecting while still preserving detection accuracy with a minor drop of 1.25% in mean average precision. The integration of these models onto the NVIDIA Jetson Nano made this approach compatible for future autonomous forklifts and showcases the potential of the technique to improve industrial automation. This study demonstrates a useful and effective method for real-time object detection in intricate warehouse settings by matching detection tasks with practical needs.

Paper Citation

in Harvard Style

Tadjine C., Ouafi A., Taleb-Ahmed A. and El Hillali Y. (2025). Class-Specific Dataset Splitting for YOLOv8: Improving Real-Time Performance in NVIDIA Jetson Nano for Faster Autonomous Forklifts. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 788-793. DOI: 10.5220/0013308700003905

in Bibtex Style

@conference{icpram25,
author={Chaouki Tadjine and Abdelkrim Ouafi and Abdelmalik Taleb-Ahmed and Yassin El Hillali},
title={Class-Specific Dataset Splitting for YOLOv8: Improving Real-Time Performance in NVIDIA Jetson Nano for Faster Autonomous Forklifts},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={788-793},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013308700003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Class-Specific Dataset Splitting for YOLOv8: Improving Real-Time Performance in NVIDIA Jetson Nano for Faster Autonomous Forklifts
SN - 978-989-758-730-6
AU - Tadjine C.
AU - Ouafi A.
AU - Taleb-Ahmed A.
AU - El Hillali Y.
PY - 2025
SP - 788
EP - 793
DO - 10.5220/0013308700003905
PB - SciTePress