WO2024037673A1 - Système d'intelligence artificielle pour pronostiquer la distance d'objets - Google Patents

Système d'intelligence artificielle pour pronostiquer la distance d'objets Download PDF

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Publication number
WO2024037673A1
WO2024037673A1 PCT/CO2023/000013 CO2023000013W WO2024037673A1 WO 2024037673 A1 WO2024037673 A1 WO 2024037673A1 CO 2023000013 W CO2023000013 W CO 2023000013W WO 2024037673 A1 WO2024037673 A1 WO 2024037673A1
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WO
WIPO (PCT)
Prior art keywords
artificial intelligence
process according
intelligence process
input nodes
neural network
Prior art date
Application number
PCT/CO2023/000013
Other languages
English (en)
Spanish (es)
Inventor
Reynaldo VILLARREAL GONZALEZ
Paola AMAR SEPÚLVEDA
Juan PESTANA NOBLES
Roberto PESTANA
Sindy CHAMORRO
Original Assignee
Universidad Simón Bolívar
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.)
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Publication date
Application filed by Universidad Simón Bolívar filed Critical Universidad Simón Bolívar
Publication of WO2024037673A1 publication Critical patent/WO2024037673A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • G01S17/10—Systems determining position data of a target for measuring distance only using transmission of interrupted, pulse-modulated waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/483—Details of pulse systems
    • G01S7/484—Transmitters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/483—Details of pulse systems
    • G01S7/486—Receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/497—Means for monitoring or calibrating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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

Definitions

  • the present invention belongs to the field of measurements implemented through artificial intelligence. Particularly, it is related to measurements carried out in underwater environments.
  • Patent CN112257566 “Artificial intelligence method for distance measurement and target identification based on big data” was also found.
  • the invention belongs to the technical field of target recognition and distance measurement, and in particular relates to an artificial intelligence target recognition and distance measurement method based on big data, which is higher in recognition accuracy and higher in speed .
  • the method comprises the following steps: preprocessing of a received signal; generating an anchor box to identify the target through a K-Means clustering algorithm; building a convolutional neural network branch, and defining a parameter layer of the convolutional neural network; and testing the arrival of the chirp signal in the neural network evaluation model using the test set, outputting a signal arrival time estimation result of the chirp signal and obtaining the horizontal distance between the target and the receiver through the input image information.
  • the invention that is intended to be protected does not work based on the calculation of the travel time of a signal. This constitutes an important technical advantage since it does not require a light signal, which has numerous drawbacks when it comes to aquatic media that affect its reflection and refraction, thus generating calculation errors in the distance to be measured.
  • the solution includes the use of artificial intelligence that is made up of a neural network that processes the data of an image captured by a simple camera and, based on certain image conditioning processes, is capable of predicting the distance that exists between the lens of the device with which the image was captured and the objective.
  • Figure 2 Shows a “black box” type diagram of the inputs and outputs of the implemented process. Detailed description of the invention
  • the solution comprises the use of artificial intelligence that is composed of a multilayer neural network with a layer with at least five input nodes, at least one hidden layer and a layer with at least one output node.
  • the training of the model is carried out by introducing the variables that will later be collected by the input nodes and assigning a calculated and true value in centimeters for each combination of parameters.
  • the training is complemented by a compilation function to which an optimizer consisting of an extension of stochastic gradient descent (Adam's optimization algorithm) is associated. Additionally, a statistical analysis algorithm is applied to determine the average squared error, which allows you to differentiate the estimated value from the real one.
  • the process begins with the collection of data obtained by means of any image capture device that is capable of operating in underwater environments.
  • the collected data is divided into five values, four of which are preprocessed to obtain the corresponding functions provided to the input nodes and which are characterized as follows.
  • a first node receives the pixel stress level obtained through a Laplacian method, where a separating linear filter is applied that executes a mathematical operation for each row and each column, then each value is multiplied by a delta value. This is done for each axis (x, y) and the final result is made into 2 matrices. The 2 resulting matrices are added and resulting in a single matrix to which an average is applied, and results in a numerical value.
  • the second node receives the degree of standard deviation of the stress value of the pixels of the matrix, where the Laplace transform is applied on the selected area of interest, this transform is nothing more than the multiplication of matrices under rules of the first and second derivative. This procedure results in a matrix that goes through a standard deviation method and finally this value is squared in order to obtain the variance.
  • the third node receives the obtaining of the second derivative in each of the axes; from the application of the tenengrad method or TENG algorithm which is based on the application of the first and second derivative over the defined area of interest. This is done for both axes (x,y) and finally the average is obtained as a numerical value.
  • the fourth node receives the normalized variance level, for this the standard deviation is applied to the defined area of interest. With this standard deviation, the variance is then obtained and divided by the average obtained from this area.
  • the fifth and last node receives the height of the number of pixels of the object as represented in the matrix, but does not preprocess the received data.
  • the collected data is processed in the hidden layer that transmits the results to the output node.
  • the results are communicated by the output layer in a size value in cm.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Computing Systems (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Multimedia (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Studio Devices (AREA)

Abstract

La solution comprend l'utilisation d'une intelligence artificielle constituée d'un réseau neuronal qui traite les données d'une image capturée par une caméra simple et à partir de certains procédés de conditionnement de l'image est apte à pronostiquer la distance qui existe entre la lentille du dispositif avec lequel l'image a été capturée et l'objectif.
PCT/CO2023/000013 2022-08-17 2023-08-16 Système d'intelligence artificielle pour pronostiquer la distance d'objets WO2024037673A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CONC2022/0011603 2022-08-17
CONC2022/0011603A CO2022011603A1 (es) 2022-08-17 2022-08-17 Proceso de inteligencia artificial para pronosticar el tamaño de objetos

Publications (1)

Publication Number Publication Date
WO2024037673A1 true WO2024037673A1 (fr) 2024-02-22

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PCT/CO2023/000013 WO2024037673A1 (fr) 2022-08-17 2023-08-16 Système d'intelligence artificielle pour pronostiquer la distance d'objets

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CO (1) CO2022011603A1 (fr)
WO (1) WO2024037673A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10733755B2 (en) * 2017-07-18 2020-08-04 Qualcomm Incorporated Learning geometric differentials for matching 3D models to objects in a 2D image
US20200294310A1 (en) * 2019-03-16 2020-09-17 Nvidia Corporation Object Detection Using Skewed Polygons Suitable For Parking Space Detection
US20210248812A1 (en) * 2021-03-05 2021-08-12 University Of Electronic Science And Technology Of China Method for reconstructing a 3d object based on dynamic graph network
US11126915B2 (en) * 2018-10-15 2021-09-21 Sony Corporation Information processing apparatus and information processing method for volume data visualization
US20210350560A1 (en) * 2019-01-24 2021-11-11 Imperial College Innovations Limited Depth estimation
US20220084234A1 (en) * 2020-09-17 2022-03-17 GIST(Gwangju Institute of Science and Technology) Method and electronic device for identifying size of measurement target object

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10733755B2 (en) * 2017-07-18 2020-08-04 Qualcomm Incorporated Learning geometric differentials for matching 3D models to objects in a 2D image
US11126915B2 (en) * 2018-10-15 2021-09-21 Sony Corporation Information processing apparatus and information processing method for volume data visualization
US20210350560A1 (en) * 2019-01-24 2021-11-11 Imperial College Innovations Limited Depth estimation
US20200294310A1 (en) * 2019-03-16 2020-09-17 Nvidia Corporation Object Detection Using Skewed Polygons Suitable For Parking Space Detection
US20220084234A1 (en) * 2020-09-17 2022-03-17 GIST(Gwangju Institute of Science and Technology) Method and electronic device for identifying size of measurement target object
US20210248812A1 (en) * 2021-03-05 2021-08-12 University Of Electronic Science And Technology Of China Method for reconstructing a 3d object based on dynamic graph network

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