FR3092546B1 - Identification de zones roulables avec prise en compte de l’incertitude par une méthode d’apprentissage profond - Google Patents

Identification de zones roulables avec prise en compte de l’incertitude par une méthode d’apprentissage profond Download PDF

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Publication number
FR3092546B1
FR3092546B1 FR1901448A FR1901448A FR3092546B1 FR 3092546 B1 FR3092546 B1 FR 3092546B1 FR 1901448 A FR1901448 A FR 1901448A FR 1901448 A FR1901448 A FR 1901448A FR 3092546 B1 FR3092546 B1 FR 3092546B1
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rollable
input image
zones
uncertainty
consideration
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FR1901448A
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FR3092546A1 (fr
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Sylvaine Picard
Andrei Bursuc
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Safran SA
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Safran SA
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Priority to FR1901448A priority Critical patent/FR3092546B1/fr
Priority to PCT/FR2020/050268 priority patent/WO2020165544A1/fr
Publication of FR3092546A1 publication Critical patent/FR3092546A1/fr
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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
    • 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
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
    • G06F18/2178—Validation; Performance evaluation; Active pattern learning techniques based on feedback of a supervisor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • 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
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Multimedia (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

La présente invention concerne un procédé d’identification de zones roulables dans au moins une image d’un terrain, caractérisé en ce qu’il comprend l’implémentation des étapes suivantes dans une unité de traitement (4) : - apprentissage supervisé d’au moins un réseau de neurones (R2), à partir d’une base de données d’apprentissage comprenant des images d’apprentissages de terrain ayant été annotées par au moins deux opérateurs en vue d’identifier des zones roulables, - segmentation au moyen de l’au moins un réseau de neurones (R2), d’une image d’entrée (I1) pour obtenir une carte probabiliste (I3) de présence d’au moins une zone roulable dans ladite image d’entrée (I1), ladite segmentation comprenant une estimation d’une probabilité de présence d’une zone roulable dans l’image d’entrée (I1), de manière à obtenir une carte probabiliste (I3) définie par une matrice dont chaque élément correspond à une probabilité de présence d’au moins une zone roulable sur un pixel de l’image d’entrée (I1). Figure pour l’abrégé : Fig. 5
FR1901448A 2019-02-13 2019-02-13 Identification de zones roulables avec prise en compte de l’incertitude par une méthode d’apprentissage profond Active FR3092546B1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
FR1901448A FR3092546B1 (fr) 2019-02-13 2019-02-13 Identification de zones roulables avec prise en compte de l’incertitude par une méthode d’apprentissage profond
PCT/FR2020/050268 WO2020165544A1 (fr) 2019-02-13 2020-02-13 Identification de zones roulables avec prise en compte de l'incertitude par une méthode d'apprentissage profond

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR1901448A FR3092546B1 (fr) 2019-02-13 2019-02-13 Identification de zones roulables avec prise en compte de l’incertitude par une méthode d’apprentissage profond
FR1901448 2019-02-13

Publications (2)

Publication Number Publication Date
FR3092546A1 FR3092546A1 (fr) 2020-08-14
FR3092546B1 true FR3092546B1 (fr) 2022-05-20

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FR1901448A Active FR3092546B1 (fr) 2019-02-13 2019-02-13 Identification de zones roulables avec prise en compte de l’incertitude par une méthode d’apprentissage profond

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FR (1) FR3092546B1 (fr)
WO (1) WO2020165544A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112785619A (zh) * 2020-12-31 2021-05-11 大连海事大学 一种基于视觉感知的无人水下航行器自主循迹方法
CN114674338B (zh) * 2022-04-08 2024-05-07 石家庄铁道大学 基于分层输入输出和双注意力跳接的道路可行驶区域精细推荐方法

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10176388B1 (en) * 2016-11-14 2019-01-08 Zoox, Inc. Spatial and temporal information for semantic segmentation
US20180157972A1 (en) * 2016-12-02 2018-06-07 Apple Inc. Partially shared neural networks for multiple tasks

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WO2020165544A1 (fr) 2020-08-20
FR3092546A1 (fr) 2020-08-14

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