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    PRUNING FILTERS FOR EFFICIENT CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE RECOGNITION IN SURVEILLANCE APPLICATIONS
    11.
    发明申请
    PRUNING FILTERS FOR EFFICIENT CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE RECOGNITION IN SURVEILLANCE APPLICATIONS 审中-公开

    公开(公告)号：US20180336468A1

    公开(公告)日：2018-11-22

    申请号：US15979500

    申请日：2018-05-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Asim Kadav ,  Igor Durdanovic ,  Hans Peter Graf

    IPC: G06N3/08 ,  G06N5/04 ,  G06K9/00

    Abstract: Systems and methods for pruning a convolutional neural network (CNN) for surveillance with image recognition are described, including extracting convolutional layers from a trained CNN, each convolutional layer including a kernel matrix having at least one filter formed in a corresponding output channel of the kernel matrix, and a feature map set having a feature map corresponding to each filter. An absolute kernel weight is determined for each kernel and summed across each filter to determine a magnitude of each filter. The magnitude of each filter is compared with a threshold and removed if it is below the threshold. A feature map corresponding to each of the removed filters is removed to prune the CNN of filters. The CNN is retrained to generate a pruned CNN having fewer convolutional layers to efficiently recognize and predict conditions in an environment being surveilled.

    Pruning filters for efficient convolutional neural networks for image recognition in vehicles
    15.
    发明授权
    Pruning filters for efficient convolutional neural networks for image recognition in vehicles 有权

    公开(公告)号：US10755136B2

    公开(公告)日：2020-08-25

    申请号：US15979509

    申请日：2018-05-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Asim Kadav ,  Igor Durdanovic ,  Hans Peter Graf

    IPC: G06K9/46 ,  G06K9/62 ,  G06K9/66 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06N5/04

    Abstract: Systems and methods for surveillance are described, including an image capture device configured to mounted to an autonomous vehicle, the image capture device including an image sensor. A storage device is included in communication with the processing system, the storage device including a pruned convolutional neural network (CNN) being trained to recognize obstacles in a road according to images captured by the image sensor by training a CNN with a dataset and removing filters from layers of the CNN that are below a significance threshold for image recognition to produce the pruned CNN. A processing device is configured to recognize the obstacles by analyzing the images captured by the image sensor with the pruned CNN and to predict movement of the obstacles such that the autonomous vehicle automatically and proactively avoids the obstacle according to the recognized obstacle and predicted movement.

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