What question did this study set out to answer?

The central aim is to benchmark the performance of YOLO versions 8 to 12 on various single-board computers.

July 15, 2026Open Access

Benchmarking YOLOv8–YOLOv12 for Real-Time Object Detection on Single-Board Computers

OSOmar ShalashAjman University EKEsraa KhatabHeriot-Watt University Dubai AEAhmed El-Agamy

Key Points

The central aim is to benchmark the performance of YOLO versions 8 to 12 on various single-board computers.
Evaluated YOLO models 8 through 12 on Raspberry Pi4/5, NVIDIA Jetson Nano, Jetson Orin, and LattePanda.
Assessed key performance metrics including inference speed, detection accuracy, RAM usage, and computational complexity under different power modes.
YOLOv12 achieved the highest detection accuracy with a mAP of 75% on the Jetson Orin.
Inference speeds reached up to 45 FPS on Raspberry Pi5 when using YOLOv10.
Average RAM usage varied, with YOLOv8 requiring 1.2 GB and YOLOv11 requiring 1.6 GB on Jetson Nano.

Abstract

The YOLO (You Only Look Once) object detection models have undergone rapid evolution, with each version introducing architectural enhancements aiming to improve speed, accuracy, and deployment. Simultaneously, single-board computers (SBCs) have advanced to support increasingly complex AI models in edge environments. This study presents a comprehensive benchmarking of YOLO versions 8 through 12 across a range of SBCs, including Raspberry Pi4/5, NVIDIA Jetson Nano, Jetson Orin, and LattePanda, under different power modes. Key performance metrics, including inference speed (FPS), detection accuracy (mAP), RAM usage, and computational complexity (FLOPs), are evaluated. These findings offer practical insights for developers and researchers to select optimal YOLO variants and SBC configurations for real-time edge deployment.

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