Optimizing YOLOv8 for Real-Time Performance in Humanoid Soccer Robots with OpenVINO

Abstract

Humanoid soccer robots require fast and accurate vision systems for effective real-time decision-making. YOLOv8 (You Only Look Once) is a leading deep learning-based object detection method known for its balance of speed and accuracy. This research explores optimizing YOLOv8 performance using OpenVINO (Open Visual Inference & Neural Network Optimization), a toolkit designed to accelerate and enhance deep learning models on Intel hardware. We assessed performance improvements in terms of frames per second (FPS) and accuracy using an NUC1017FNH mini PC. Our experiments utilized a dataset of over 12,000 images categorized into five classes: orange ball, goalpost, L line, T line, and X line. The results show that OpenVINO effectively doubles YOLOv8’s inference speed, reducing the average processing time from 36 ms to 16 ms and increasing FPS from 25 to 32, while maintaining detection accuracy. This optimization significantly boosts the speed and responsiveness of vision systems in humanoid robots, ensuring robust performance in real-time scenarios.