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相关实验视频

Updated: Jul 9, 2025

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基于深度学习的动态和实时对象检测,用于家庭服务机器人.

Yangqing Ye1, Xiaolon Ma2, Xuanyi Zhou1

  • 1College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

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本研究介绍了家庭服务机器人的动态实时物体检测算法,增强了它们识别运动模糊和遮蔽图像中的物体的能力. 新方法显著提高了检测准确度和处理速度,以实现高效的室内导航和任务完成.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 家庭服务机器人需要精确的对象识别和定位,以有效执行任务.
  • 来自移动传感器的运动模糊和遮蔽图像对物体检测构成重大挑战.
  • 检测日常生活中常见的小物体和封闭物体特别困难.

研究的目的:

  • 为家庭服务机器人开发动态和实时物体检测算法.
  • 为应对室内环境中运动模糊和物体封闭所带来的挑战.
  • 为了提高服务机器人应用的对象识别的准确性和效率.

主要方法:

  • 提出了一种新的动态和实时物体检测算法,包括图像消除模糊和物体检测组件.
  • 开发了DA-Multi-DCGAN算法,用于使用动态调整和多式联接来消除动作模糊图像的模糊.
  • 引入了AT-LI-YOLO方法用于检测小物体和封闭物体,结合了注意力机制和轻量级网络结构.

主要成果:

  • 与DeblurGAN.com相比,DA-Multi-DCGAN提高了5.07的峰值信号噪声比 (PSNR) 和0.022的结构相似性 (SSIM).
  • 与YOLOv3相比,AT-LI-YOLO的平均精度 (mAP) 提高了3.19%,在检测小物体 (19.12%) 和封闭物体 (29.52%) 方面取得了显著的进步.
关键词:
AT-LI-YOLO 的意思是说DA-多个DCGAN的DA.室内服务机器人室内服务机器人实时物体检测实时物体检测

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  • 集成的算法使用TensorRT实现了29毫秒的处理时间,满足服务机器人的实时要求.

    • 结论:

    • 拟议的动态和实时物体检测算法有效地处理运动模糊和阻塞.
    • 该算法在家庭服务机器人应用中对物体检测的准确性和效率方面表现出卓越的性能.
    • 开发的方法使服务机器人在复杂的室内环境中能够更顺利,更可靠地运行服务机器人.