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Detecting the shuttlecock for a badminton robot: A YOLO based approach

机译：检测羽毛球机器人的羽毛球：基于YOLO的方法

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摘要

The ability to identify objects of interest from digital visual signals is critical for many applications of intelligent systems. For such object detection task, accuracy and computational efficiency are two important aspects, especially for applications with real-time requirement. In this paper, we study shuttlecock detection problem of a badminton robot, which is very challenging since the shuttlecock often moves fast in complex contexts, and must be detected precisely in real time so that the robot can plan and execute its following movements. To this end, we propose two novel variants of Tiny YOLOv2, a well-known deep learning based detector. We first modify the loss function to adaptively improve the detection speed for small objects such as shuttlecock. We then modify the architecture of Tiny YOLOv2 to retain more semantic information of small objects, so as to further improve the performance. Experimental results show that the proposed networks can achieve high detection accuracy with the fastest speed, compared with state-of-the-art deep detectors such as Faster R-CNN, SSD, Tiny YOLOv2, and YOLOv3. Our methods could be potentially applied to other tasks of detecting high-speed small objects.
机译：识别来自数字视觉信号的感兴趣对象的能力对于许多智能系统的应用至关重要。对于此类对象检测任务，准确性和计算效率是两个重要方面，特别是对于具有实时要求的应用。在本文中，我们研究了羽毛球机器人的Shuttlecock检测问题，这是非常具有挑战性，因为Shuttlecock经常在复杂的上下文中快速移动，并且必须实时地检测到，以便机器人可以计划和执行其以下运动。为此，我们提出了两种微小的YOLOV2的新型变种，这是一种众所周知的深基于学习的探测器。我们首先修改损耗功能以自适应地提高诸如Shuttlecock等小物体的检测速度。然后，我们修改了微小的YOLOV2的体系结构，以保留更多的小物体的语义信息，以便进一步提高性能。实验结果表明，该网络可以以最快的速度达到高检测精度，与最先进的深度探测器相比，如更快的R-CNN，SSD，微小的YOLOV2和YOLOV3。我们的方法可能会应用于检测高速小物体的其他任务。

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  • 来源
    《Expert systems with applications》 |2021年第2期|113833.1-113833.7|共7页
  • 作者

    Zhiguang Cao; Tingbo Liao; Wen Song; Zhenghua Chen; Chongshou Li;

  • 作者单位

    Department of Industrial Systems Engineering and Management National University of Singapore Singapore;

    School of Automation Guangdong University of Technology China;

    Institute of Marine Science and Technology Shandong University China;

    Institute for Infocomm Research (I2R) Singapore;

    Department of Industrial Systems Engineering and Management National University of Singapore Singapore;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Deep learning; Object detection; YOLO; Badminton robot;

    机译：深度学习;对象检测;yolo;羽毛球机器人;
  • 入库时间 2022-08-19 02:10:17

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