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.

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.