公开(公告)号：US11663815B2

公开(公告)日：2023-05-30

申请号：US17191338

申请日：2021-03-03

Applicant: Infosys Limited

Inventor： Ujwal Bhate ,  Ninad Kulkarni ,  Shaurya Dwivedi

IPC: G06T7/00 ,  G06K9/62 ,  G06V10/94

CPC classification number: G06T7/0008 ,  G06K9/6257 ,  G06K9/6262 ,  G06K9/6269 ,  G06V10/95 ,  G06V10/955 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30136 ,  G06V2201/06

Abstract: Examples of the present invention provides a method and system for inspection of heat recovery steam generator (HRSG) equipment to identify defects and damages using computer vision and deep learning techniques. The method comprising capturing one or more input frames by one or more input devices, classifying the one or more input frames by a scenario classifier to identify a scenario type based on a first modelled data prepared by training one or more deep neural networks (DNN), selecting at least one damage detector based on the identified scenario type, identifying one or more damage types by the at least one damage detector based on second modelled data prepared by training the one or more DNN and displaying one or more output frame indicating the identified one or more damage types of the HRSG equipment.

公开(公告)号：US20210304400A1

公开(公告)日：2021-09-30

申请号：US17191338

申请日：2021-03-03

Applicant: Infosys Limited

Inventor： Ujwal Bhate ,  Ninad Kulkarni ,  Shaurya Dwivedi

IPC: G06T7/00 ,  G06K9/62 ,  G06K9/00

Abstract: Examples of the present invention provides a method and system for inspection of heat recovery steam generator (HRSG) equipment to identify defects and damages using computer vision and deep learning techniques. The method comprising capturing one or more input frames by one or more input devices, classifying the one or more input frames by a scenario classifier to identify a scenario type based on a first modelled data prepared by training one or more deep neural networks (DNN), selecting at least one damage detector based on the identified scenario type, identifying one or more damage types by the at least one damage detector based on second modelled data prepared by training the one or more DNN and displaying one or more output frame indicating the identified one or more damage types of the HRSG equipment.