Authors:
Chaouki Tadjine
1
;
2
;
Abdelkrim Ouafi
1
;
Abdelmalik Taleb-Ahmed
2
and
Yassin El Hillali
2
Affiliations:
1
Univ. Mohamed Khider Biskra, Department of Electrical Engineering, Faculty of Sciences and Technology, LVSC - Lab. Vision et Systèmes de Communication, 07000, Biskra, Algeria
;
2
Univ. Polytechnique Hauts-de-France, CNRS, Univ. Lille, UMR 8520 - IEMN - Institut d’Electronique de Microelectronique et de Nanotechnologie, Valenciennes, F-59313, Hauts-de-France, France
Keyword(s):
YOLOv8, LOCO Dataset, Object Detection, Autonomous Forklifts, Real-Time Inference, NVIDIA Jetson Nano.
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 dis
tance 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.
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