دانلود مقاله انگلیسی رایگان:شبکه عمیق کارآمد برای شناسایی شیء مبتنی بر دید در برنامه های کاربردی رباتیک - 2017
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  • Efficient deep network for vision-based object detection in robotic applications Efficient deep network for vision-based object detection in robotic applications
    Efficient deep network for vision-based object detection in robotic applications

    سال انتشار:

    2017


    عنوان انگلیسی مقاله:

    Efficient deep network for vision-based object detection in robotic applications


    ترجمه فارسی عنوان مقاله:

    شبکه عمیق کارآمد برای شناسایی شیء مبتنی بر دید در برنامه های کاربردی رباتیک


    منبع:

    Sciencedirect - Elsevier - Neurocomputing, 245 (2017) 31-45. doi:10.1016/j.neucom.2017.03.050


    نویسنده:

    Keyu Lu, Xiangjing An , Jian Li , Hangen He


    چکیده انگلیسی:

    Article history:Received 17 June 2016Revised 11 February 2017Accepted 17 March 2017Available online 24 March 2017 Communicated by Dr Zhang ZhaoxiangKeywords:Deep network Object detection Computer vision Robotic application MPGAVision-based object detection is essential for a multitude of robotic applications. However, it is also a challenging job due to the diversity of the environments in which such applications are required to oper- ate, and the strict constraints that apply to many robot systems in terms of run-time, power and space. To meet these special requirements of robotic applications, we propose an efficient deep network for vision-based object detection. More specifically, for a given image captured by a robot mount camera, we first introduce a novel proposal layer to efficiently generate potential object bounding-boxes. The pro- posal layer consists of efficient on-line convolutions and effective off-line optimization. Afterwards, we construct a robust detection layer which contains a multiple population genetic algorithm-based con- volutional neural network (MPGA-based CNN) module and a TLD-based multi-frame fusion procedure. Unlike most deep learning based approaches, which rely on GPU, all of the on-line processes in our sys- tem are able to run efficiently without GPU support. We perform several experiments to validate each component of our proposed object detection approach and compare the approach with some recently published state-of-the-art object detection algorithms on widely used datasets. The experimental results demonstrate that the proposed network exhibits high efficiency and robustness in object detection tasks.© 2017 Elsevier B.V. All rights reserved.
    Keywords: Deep network | Object detection | Computer vision | Robotic application | MPGA


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 15
    حجم فایل: 3737 کیلوبایت

    قیمت: رایگان


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