用YOLOv9构建机器人通用视觉模块，提升目标检测鲁棒性。
A Modular Object Detection System for Humanoid Robots Using YOLO
- 基于YOLOv9设计可适配机器人的模块化视觉系统
- 在FIRA人形机器人竞赛数据集上实现高精度检测
- 相比传统几何方法更稳定，适合复杂环境应用
在机器人领域，计算机视觉仍是制约发展的关键瓶颈，许多任务受限于低效的视觉系统。本文提出一种基于YOLOv9的通用视觉模块，该框架针对计算资源受限的机器人环境进行了优化。模型在专为FIRA人形机器人竞赛（Hurocup）定制的数据集上进行训练，并在ROS1中通过虚拟环境实现与YOLO的兼容性。采用帧率（FPS）和平均精度均值（mAP）等指标评估性能，结果表明：相较于现有几何框架，在静态与动态场景下，该模型虽计算开销更高，但检测精度相当且鲁棒性显著提升。
原文摘要 · Abstract (English)
Within the field of robotics, computer vision remains a significant barrier to progress, with many tasks hindered by inefficient vision systems. This research proposes a generalized vision module leveraging YOLOv9, a state-of-the-art framework optimized for computationally constrained environments like robots. The model is trained on a dataset tailored to the FIRA robotics Hurocup. A new vision module is implemented in ROS1 using a virtual environment to enable YOLO compatibility. Performance is evaluated using metrics such as frames per second (FPS) and Mean Average Precision (mAP). Performance is then compared to the existing geometric framework in static and dynamic contexts. The YOLO model achieved comparable precision at a higher computational cost then the geometric model, while providing improved robustness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。