篮球机器人对象检测和距离测量基于ROS和IBN-YOLOv5s算法
1Gannan Normal University, Ganzhou, China.
PloS one
|November 21, 2024
概括
这项研究提高了篮球机器人的物体检测,使用了改进的YOLOv5s算法与激光检测. 新系统显著提高了识别精度,并为机器人决策提供了更好的数据.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 传统的篮球机器人难以识别物体,影响目标识别和距离测量.
- 自主机器人研究需要强大的感知系统来完成诸如物体检测等任务.
研究的目的:
- 改进篮球机器人的物体检测系统.
- 通过更好的感知来提高篮球机器人在比赛中的表现.
主要方法:
- 使用机器人操作系统设计了一个篮球机器人物体检测系统.
- 集成了一个算法,将YOLOv5s与激光检测相结合.
- 引入了一个实例批量规范化网络模块,以增强YOLOv5s的通用化.
主要成果:
- 实现了高性能指标:在欧盟 (IoU) 上的交点为0.96,信号噪声比为0.98.
- 在与0.95的回忆曲线面积和0.9789.9的F1值的比较模型中表现出优异的性能.
- 在多个对象中,获得了95.87%的平均分类准确率和97.05%的平均校准盒准确率.
结论:
- 改进的算法为篮球机器人中高效的物体检测和识别提供了强大的性能.
- 增强的感知能力为机器人决策和行动计划提供可靠的数据.
- 这项研究显著提高了篮球机器人的整体技术能力.
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