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公开(公告)号：US12227212B2
公开(公告)日：2025-02-18
申请号：US17330591
申请日：2021-05-26
Applicant: University of South Carolina
Abstract: Systems, methods and devices for a computer vision-based pixel-level rail components detection system using an improved one-stage instance segmentation model and prior knowledge, aiming to inspect railway components in a rapid, accurate, and convenient fashion.
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公开(公告)号：US11821848B2
公开(公告)日：2023-11-21
申请号：US17332067
申请日：2021-05-27
Applicant: University of South Carolina
Inventor： Yu Qian
CPC classification number: G01N21/9515 , G01N21/8806 , E01B9/06
Abstract: Described herein are a low-cost, non-destructive, and contact-free intelligent inspection system that is field-deployable on a geometry car, high-rail vehicle, or other types of track inspection platforms to identify broken railway/railroad spikes in real-time.
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公开(公告)号：US20230314148A1
公开(公告)日：2023-10-05
申请号：US18161155
申请日：2023-01-30
Applicant: University of South Carolina
Inventor： Yu Qian , Yuche Chen
IPC: G01C21/34
CPC classification number: G01C21/3446 , G01C21/3461 , G01C21/3492
Abstract: Described herein are systems and methods for identifying an optimal travel route for first travelers, such as commuters, first responders, etc., that considers both potential railroad crossing blockages in addition to street traffic congestion conditions to provide real-time optimal vehicle routing based on real-time street and railroad crossing conditions.
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Patent Agency Ranking
公开(公告)号：US20210396685A1
公开(公告)日：2021-12-23
申请号：US17332067
申请日：2021-05-27
Applicant: University of South Carolina
Inventor： Yu Qian
Abstract: Described herein are a low-cost, non-destructive, and contact-free intelligent inspection system that is field-deployable on a geometry car, high-rail vehicle, or other types of track inspection platforms to identify broken railway/railroad spikes in real-time.
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公开(公告)号：US20240248047A1
公开(公告)日：2024-07-25
申请号：US18513934
申请日：2023-11-20
Applicant: University of South Carolina
Inventor： Yu Qian
CPC classification number: G01N21/9515 , G01N21/8806 , E01B9/06
Abstract: Described herein are a low-cost, non-destructive, and contact-free intelligent inspection system that is field-deployable on a geometry car, high-rail vehicle, or other types of track inspection platforms to identify broken railway/railroad spikes in real-time.
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公开(公告)号：US20230314383A1
公开(公告)日：2023-10-05
申请号：US18187036
申请日：2023-03-21
Applicant: University of South Carolina
CPC classification number: G01N29/046 , G01N29/4436 , G01N29/4445 , G01N2291/0234 , G01N2291/2626
Abstract: Described herein are systems, methods and devices for gathering sound emission data from railway spikes in order to determine if a spike is undamaged, damaged, or broken via initiating sound waves in the spike and analyzing same to determine the structural integrity of the spike being tested.
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公开(公告)号：US20230286556A1
公开(公告)日：2023-09-14
申请号：US18179691
申请日：2023-03-07
Applicant: University of South Carolina
CPC classification number: B61L23/042 , B64U10/00 , G08G5/0069 , G08G5/045 , G05D1/106 , H04N7/183 , G06T7/0002 , G06T7/50 , B64U2201/10 , B64U2101/26
Abstract: Described herein is a fully autonomous drone-based track inspection system that does not rely on GPS but instead uses optical images taken from the drone to identify the railroad track and navigate the drone to cruise along the track to perform track inspection tasks; track images are taken by the onboard drone camera and processed to provide both navigation information for autonomous drone flight control and track component health evaluation.
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公开(公告)号：US20210370993A1
公开(公告)日：2021-12-02
申请号：US17330591
申请日：2021-05-26
Applicant: University of South Carolina
Abstract: Systems, methods and devices for a computer vision-based pixel-level rail components detection system using an improved one-stage instance segmentation model and prior knowledge, aiming to inspect railway components in a rapid, accurate, and convenient fashion.
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