基于YOLOv8s的改进方法来提高动态物体检测的准确性和速度
Zhiguo Liu1, Enzheng Zhang1, Qian Ding1
1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究增强了YOLOv8s模型的动态对象跟踪,提高了检测准确度和速度. 这种新方法对于机器人技能学习和人工智能应用至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 准确的动态对象检测和跟踪对于机器人技能学习和概括至关重要.
- 像YOLOv8s这样的现有模型需要改进,以在动态场景中提高性能.
研究的目的:
- 为了提高动态物体跟踪的YOLOv8s模型的检测准确度和跟踪速度.
- 在机器人技术中引入实时,精确的物体运动分析的新机制.
主要方法:
- 在YOLOv8的骨干中集成一个聚焦线性注意力机制,以增强检测.
- 将Ghost模块集成到YOLOv8的子网络中,以提高追踪速度.
- 通过映射跨的动态物体运动来实现轨迹跟踪.
主要成果:
- 与MS-COCO和定制数据集上的基线YOLOv8s相比,拟的方法显示出更高的检测准确度和处理速度.
- 实验证实了该方法在不同速度检测和跟踪物体方面的有效性.
- 在动态物体检测任务中改进性能的验证.
结论:
- 增强的YOLOv8s模型在动态物体检测和跟踪准确性和速度方面提供了显著的改进.
- 这项研究为人工智能驱动的机器人提供了宝贵的见解,特别是在机器人技能学习和概括方面.
- 拟议的方法是推动动态物体检测技术的强有力的参考.
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