Two-stage deep learning framework for detecting the condition of rail car coupler systems
US11507779B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 24, 2020 |
| Grant date | Nov 22, 2022 |
| Priority date | — |
| Expiry date | Aug 25, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.