计算机应用 ›› 2019, Vol. 39 ›› Issue (8): 2230-2234.DOI: 10.11772/j.issn.1001-9081.2018122595

• 人工智能 • 上一篇    下一篇

基于Movidius神经计算棒的行人检测方法

张洋硕, 苗壮, 王家宝, 李阳   

  1. 陆军工程大学 指挥控制工程学院, 南京 210007
  • 收稿日期:2019-01-02 修回日期:2019-04-02 出版日期:2019-08-10 发布日期:2019-04-18
  • 通讯作者: 张洋硕
  • 作者简介:张洋硕(1995-),男,河南三门峡人,硕士研究生,主要研究方向:计算机视觉、目标检测;苗壮(1976-),男,辽宁辽阳人,副教授,博士,主要研究方向:人工智能;王家宝(1985-),男,安徽肥西人,讲师,博士,主要研究方向:模式识别、图像检索;李阳(1984-),男,河北廊坊人,讲师,博士,主要研究方向:机器视觉、机器学习。
  • 基金资助:
    国家自然科学基金资助项目(61806220)。

Pedestrian detection method based on Movidius neural computing stick

ZHANG Yangshuo, MIAO Zhuang, WANG Jiabao, LI Yang   

  1. College of Command and Control Engineering, Army Engineering University, Nanjing Jiangsu 210007, China
  • Received:2019-01-02 Revised:2019-04-02 Online:2019-08-10 Published:2019-04-18
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61806220).

摘要: Movidius神经计算棒是基于USB模式的深度学习推理工具和独立的人工智能加速器,为广泛的移动和嵌入式视觉设备提供专用深度神经网络加速功能。针对深度学习的嵌入式应用,实现了一种基于Movidius神经计算棒的近实时行人目标检测方法。首先,通过改进RefineDet目标检测网络结构使模型大小和计算适应嵌入式设备的要求;然后,在行人检测数据集上对模型进行重训练,并部署于搭载Movidius神经计算棒的树莓派上;最后,在实际环境中对模型进行测试,算法达到了平均每秒4帧的处理速度。实验结果表明,基于Movidius神经计算棒,在计算资源紧张的树莓派上可完成近实时的行人检测任务。

关键词: 行人检测, 深度学习, 树莓派, Movidius, 嵌入式设备

Abstract: Movidius neural computing stick is a USB-based deep learning inference tool and a stand-alone artificial intelligence accelerator that provides dedicated deep neural network acceleration for a wide range of mobile and embedded vision devices. For the embedded application of deep learning, a near real-time pedestrian target detection method based on Movidius neural computing stick was realized. Firstly, the model size and calculation were adapted to the requirements of the embedded device by improving the RefineDet target detection network structure. Then, the model was retrained on the pedestrian detection dataset and deployed on the Raspberry Pi equipped with Movidius neural computing stick. Finally, the model was tested in the actual environment, and the algorithm achieved an average processing speed of 4 frames per second. Experimental results show that based on Movidius neural computing stick, the near real-time pedestrian detection task can be completed on the Raspberry Pi with limited computing resources.

Key words: pedestrian detection, deep learning, Raspberry Pi, Movidius, embedded device

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