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    Two-stage deep learning framework for detecting the condition of rail car coupler systems
    3.
    发明授权
    Two-stage deep learning framework for detecting the condition of rail car coupler systems 有权

    公开(公告)号：US11507779B1

    公开(公告)日：2022-11-22

    申请号：US16938102

    申请日：2020-07-24

    申请人： NORFOLK SOUTHERN CORPORATION

    发明人： Mahbod Amouie ,  Gongli Duan ,  Wei Liu ,  Ilya A. Lavrik

    IPC分类号： G06K9/62 ,  G06N20/20 ,  G06V10/25

    摘要： Systems, devices, media, and methods are presented for training a predictive model to detect objects in digital images, such as detecting whether a cotter key is present or absent in a digital photograph of a rail car coupler. The training system includes curating a plurality of training datasets, each including a number of raw images, together with a number of adjusted, augmented, and duplicate images. The predictive model includes a localization algorithm and an ensemble of models for classification. The localization algorithm is a deep convolutional neural network (CNN) which identifies a region of interest. One of more of the deep CNN classification models generates a plurality of candidate regions associated with each region of interest, thereby generating a large number of additional regions useful for training. In use, the trained predictive model is part of a detection and notification system that processes new images from the field and broadcasts a notice when an anomaly is detected.