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1.发明授权公开(公告)号：US11507779B1
公开(公告)日：2022-11-22
申请号：US16938102
申请日：2020-07-24
发明人： Mahbod Amouie , Gongli Duan , Wei Liu , Ilya A. Lavrik
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.
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公开(公告)号：US11468551B1
公开(公告)日：2022-10-11
申请号：US17549499
申请日：2021-12-13
发明人： Mahbod Amouie , Evan T. Gebhardt , Gongli Duan , Myles Grayson Akin , Wei Liu , Tianchen Wang , Mayuresh Manoj Sardesai , Ilya A. Lavrik
摘要： A computer-implemented method in which one or more processing devices perform operations may include obtaining a field image of a railcar collected from a field camera system and applying a machine-learning algorithm to the field image to generate a machine-learning algorithm output. The method may also include performing a post-processing operation on the machine-learning algorithm output to generate a filtered machine-learning algorithm output. Further, the method may include detecting a defect of the railcar using the filtered machine-learning algorithm output.
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