MX2018000340A - Generacion de datos de entrenamiento para deteccion de filtracion de vehiculo automatica. - Google Patents

Generacion de datos de entrenamiento para deteccion de filtracion de vehiculo automatica.

Info

Publication number
MX2018000340A
MX2018000340A MX2018000340A MX2018000340A MX2018000340A MX 2018000340 A MX2018000340 A MX 2018000340A MX 2018000340 A MX2018000340 A MX 2018000340A MX 2018000340 A MX2018000340 A MX 2018000340A MX 2018000340 A MX2018000340 A MX 2018000340A
Authority
MX
Mexico
Prior art keywords
arrival
images
departure
image
location
Prior art date
Application number
MX2018000340A
Other languages
English (en)
Inventor
Elizabeth Micks Ashley
J Jain Jinesh
Banvait Harpreetsingh
Sielly Jales Costa Bruno
Original Assignee
Ford Global Tech Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ford Global Tech Llc filed Critical Ford Global Tech Llc
Publication of MX2018000340A publication Critical patent/MX2018000340A/es

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00—Investigating fluid-tightness of structures
    • G01M3/007—Leak detector calibration, standard leaks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2431—Multiple classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/60—Editing figures and text; Combining figures or text
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G06V20/586—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of parking space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G06V20/653—Three-dimensional objects by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/275—Image signal generators from 3D object models, e.g. computer-generated stereoscopic image signals
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
    • B60R2300/80—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the intended use of the viewing arrangement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • G06T2207/30264—Parking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Multimedia (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Image Analysis (AREA)
  • Traffic Control Systems (AREA)
  • Mechanical Engineering (AREA)
  • Architecture (AREA)
  • Computer Graphics (AREA)
  • Computer Hardware Design (AREA)
  • Image Processing (AREA)
  • Emergency Alarm Devices (AREA)

Abstract

Un controlador de vehículo recibe imágenes de una cámara a la llegada y a la partida. Se puede rastrear una ubicación del vehículo y las imágenes capturadas por la cámara pueden etiquetarse con una ubicación. Una imagen de partida puede compararse con una imagen de llegada capturada más cercana a la misma ubicación que la imagen de llegada. Una imagen residual en función de una diferencia entre las imágenes de llegada y de partida se evalúa en busca de anomalías. Se determinan los atributos de la anomalía como textura, color, y similares, y la anomalía se clasifica en función de los atributos. Si la clasificación indica un fluido de automóvil, se genera una alerta. Un algoritmo de aprendizaje automático para generar clasificaciones a partir de datos de imágenes se puede entrenar usando imágenes de llegada y de partida obtenidas mediante la reproducción de un modelo tridimensional o mediante la adición de filtraciones de fluido simuladas a imágenes bidimensionales.
MX2018000340A 2017-01-11 2018-01-09 Generacion de datos de entrenamiento para deteccion de filtracion de vehiculo automatica. MX2018000340A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US15/404,031 US10296816B2 (en) 2017-01-11 2017-01-11 Generating training data for automatic vehicle leak detection

Publications (1)

Publication Number Publication Date
MX2018000340A true MX2018000340A (es) 2018-11-09

Family

ID=61190382

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2018000340A MX2018000340A (es) 2017-01-11 2018-01-09 Generacion de datos de entrenamiento para deteccion de filtracion de vehiculo automatica.

Country Status (6)

Country Link
US (1) US10296816B2 (es)
CN (1) CN108304861B (es)
DE (1) DE102018100192A1 (es)
GB (1) GB2560234B (es)
MX (1) MX2018000340A (es)
RU (1) RU2017140787A (es)

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Also Published As

Publication number Publication date
GB2560234B (en) 2022-09-28
CN108304861A (zh) 2018-07-20
GB2560234A (en) 2018-09-05
GB201800198D0 (en) 2018-02-21
US10296816B2 (en) 2019-05-21
US20180197048A1 (en) 2018-07-12
CN108304861B (zh) 2023-07-25
RU2017140787A (ru) 2019-05-23
DE102018100192A1 (de) 2018-07-12

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