WO2020083103A1 - Procédé de positionnement de véhicule sur la base d'une reconnaissance d'image de réseau neuronal profond - Google Patents

Procédé de positionnement de véhicule sur la base d'une reconnaissance d'image de réseau neuronal profond Download PDF

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Publication number
WO2020083103A1
WO2020083103A1 PCT/CN2019/111840 CN2019111840W WO2020083103A1 WO 2020083103 A1 WO2020083103 A1 WO 2020083103A1 CN 2019111840 W CN2019111840 W CN 2019111840W WO 2020083103 A1 WO2020083103 A1 WO 2020083103A1
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Prior art keywords
neural network
deep neural
road sign
vehicle
coordinate system
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PCT/CN2019/111840
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English (en)
Chinese (zh)
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冯江华
胡云卿
袁浩
林军
刘悦
游俊
熊群芳
丁驰
岳伟
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中车株洲电力机车研究所有限公司
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Priority to SG11202103814PA priority Critical patent/SG11202103814PA/en
Publication of WO2020083103A1 publication Critical patent/WO2020083103A1/fr

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    • 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
    • G06V20/582—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of traffic signs
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • 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
    • 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
    • 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
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/245—Aligning, centring, orientation detection or correction of the image by locating a pattern; Special marks for positioning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/09—Recognition of logos

Definitions

  • the invention relates to the technical field of image recognition and positioning, in particular to a vehicle positioning method based on deep neural network image recognition, and a training method of the deep neural network.
  • vehicle positioning technology mainly uses GPS technology and high-precision map matching positioning.
  • the GPS technology has the following problems when it is used: when using the ordinary GPS mode for positioning, the positioning error reaches the meter level, which cannot meet the accuracy requirements of the vehicle; if the GPS RTK mode is used, it is necessary to obtain both satellite information and ground reference positioning information It is necessary to install reference positioning communication equipment along the road. The equipment cost and use cost are high. When the vehicle enters the road section with poor satellite signal, such as dense forest or tunnel, the GPS signal is easy to be lost, thereby losing positioning information.
  • Map data needs to be established and stored on the vehicle in advance.
  • point cloud data or image data of the current environment of the vehicle is obtained through an external laser radar or camera device And match with pre-stored map data.
  • the cost of map making and software and hardware cost of matching calculation are relatively high.
  • a low-cost, high-precision vehicle positioning method is needed to provide reliable data support for vehicle positioning, pit route planning, and speed control.
  • the invention provides a vehicle positioning method based on deep neural network image recognition, and a training method of the deep neural network.
  • the invention can increase the training accuracy of the deep neural network by increasing the number of training samples and optimizing the parameters of the neural network, thereby improving the positioning accuracy of the vehicle, and the required equipment cost and use cost are low.
  • the first aspect of the present invention provides a deep neural network training method for road sign recognition, including the following steps:
  • Road sign graphic setting step set a road sign graphic on the road surface of the station inbound direction, and the distance between the marking point of the road sign graphic and the edge of the station inbound direction is L;
  • Steps for setting the camera install the camera on the vehicle, the optical axis of the camera coincides with the longitudinal centerline of the vehicle body, and the distance between the lens center of the camera and the ground is H
  • Training sample production step calculate the position coordinates of the identification point of the road sign graphic in the image coordinate system in each of the image samples, make a label set, and combine each of the image samples with the corresponding labels Set pairing to form training samples;
  • Steps for building a deep neural network on the basis of the target recognition classification deep neural network, the final classification output layer of the network is modified into an output layer composed of 2 nodes to output the position of the marking point of the road marking graphic coordinate;
  • Deep neural network training step input the training samples to the deep neural network for training.
  • the shooting time is selected at noon on a sunny day and at night on a sunny day.
  • the shooting time is selected at noon on rainy days and night on rainy days.
  • the shooting time is selected at noon on a foggy day and at night on a foggy day.
  • the photographing device photographs an image sample of the road marking pattern every 5 ° within an angle range of 5 ° to 180 ° between the optical axis and the road surface.
  • the lens parameters of the shooting device are selected so that when all the road sign graphics appear in the lens screen, the road sign graphics can occupy more than 20% of the area of the lens screen.
  • the photographing device is installed at the front roof position of the vehicle, and points in the direction of the vehicle.
  • the road sign graphic adopts a triangle, a rectangle, an arc, or other geometric element combinations that are easy to recognize.
  • the road sign graphic is a bar code or a two-dimensional code.
  • the identification point of the road identification graphic is its geometric center.
  • the deep neural network adopts ResNet50 network, and replaces the last classification output layer of the network with two fully connected layers with 1024 nodes, and the fully connected layer is connected with an output layer with 2 nodes output.
  • the deep neural network adopts a ResNet50 network, and replaces the final classification output layer of the network with two fully connected layers with 2048 nodes, and the fully connected layer is connected with an output layer with 2 nodes output.
  • the floating point data output by the two nodes belong to the closed interval of [0, 1], and the pixel coordinates can be obtained by multiplying the output floating point data by the corresponding image width and height.
  • a second aspect of the present invention provides a vehicle positioning method using the above deep neural network training method, including the following steps:
  • Road sign pattern recognition step use the deep neural network after training to identify the road sign pattern photographed during the actual pit stop of the vehicle and obtain its sign point P in the image coordinate system Position coordinates (u, v);
  • Vehicle positioning step determine the distance between the shooting device and the edge of the station in the direction of the station according to the distance between the obtained road marking graphic identification point P and the shooting device, and then combine the shooting device on the vehicle To determine the distance between the vehicle and the edge of the station into the station.
  • the marking point P of the road marking graphic is on the optical axis of the lens of the shooting device;
  • the origin of the coordinate system of the shooting device is set at the position of the imaging aperture of the shooting device, and the horizontal distance between the optical center of the lens of the shooting device and the marking point P of the road marking graphic is Z C ;
  • the positive direction of the Z axis of the camera coordinate system is selected as the forward direction of the vehicle, the positive direction of the Y axis of the camera coordinate system is selected as the downward direction of the vehicle, and the positive X axis of the camera coordinate system is positive Select the right direction of the vehicle;
  • the world coordinate system coincides with the camera coordinate system
  • the origin of the image coordinate system is on the Z axis of the camera coordinate system, and the X axis and Y axis of the image coordinate system are parallel to the X axis and Y axis of the camera coordinate system, respectively;
  • the image sample collection process is carried out in multiple periods under different lighting and weather conditions, which reduces the influence of environmental factors on the training results and improves the environmental adaptability of the deep neural network.
  • the above method can provide the distance data between the vehicle and the station, provide data support for vehicle positioning, inbound route planning, and speed control, and has the advantages of simple operation, low cost, and high reliability.
  • FIG. 1 is a flowchart of a deep neural network training method for road sign recognition
  • Figure 2 is a schematic diagram of shooting in the image sample collection step
  • FIG. 3 is a flowchart of a vehicle positioning method based on a deep neural network after training is completed
  • Figure 4 is a side view of the vehicle during the vehicle entering the station
  • 5 is a plan view of the vehicle during the vehicle entering the station
  • Fig. 6 is a schematic diagram of calculating the edge distance between the vehicle and the station in the direction of the station.
  • FIG. 1 is a flowchart of a deep neural network training method for road sign recognition provided by the present invention, including a road sign graphic setting step 101, a camera setting step 102, an image sample acquisition step 103, a training sample production step 104, Deep neural network construction step 105, deep neural network training step 106.
  • Road sign graphic setting step 101 Set a road sign graphic on the road surface of the station inbound direction, and the distance between the marking point of the road sign graphic and the edge of the station inbound direction is L.
  • the road marking graphics may be, but not limited to, triangles, rectangles, arcs, or other easily identifiable combinations of geometric elements, or text graphics, or bar codes or two-dimensional codes incorporating station-related information.
  • the identification point of the road identification graphic may be a geometric center point, vertex or other geometric feature point of the road identification graphic.
  • Shooting device setting step 102 Install a shooting device on the vehicle.
  • the lens of the shooting device points to the direction of the vehicle. It is installed on the roof of the front of the vehicle or other position where the road sign can be photographed.
  • the longitudinal symmetry centerline of the vehicle body coincides, and the distance H between the lens optical center of the shooting device and the ground is recorded.
  • the lens parameters of the shooting device are selected such that, when all the road sign graphics appear in the lens screen, the road sign graphics can occupy more than 20% of the area of the lens screen, and the larger the area, the sign point of the road sign graphics is The more precise the positioning.
  • Image sample collection step 103 Under different lighting or weather conditions, such as sunny noon and sunny night, rainy noon and rainy night, foggy noon and foggy night, the road sign pattern is photographed using the above-mentioned shooting device.
  • the shooting angle is shown in Figure 2, where the letter A represents the road sign graphic, and the angle between the optical axis of the camera and the road surface is changed in the direction of the vehicle's advance and the direction perpendicular to the direction of the vehicle's direction, so that the camera is in its An image sample of a road marking image is taken every 5 ° within an angle of 5 ° to 180 ° between the optical axis and the road surface.
  • Training sample production step 104 Calculate the position coordinates of the road marking graphics in each image sample in the image coordinate system, make a label set, and pair each image sample with the corresponding label set to form a training sample, so that Then input deep neural network for training.
  • Deep neural network construction step 105 Use a target recognition classification deep neural network, but modify the final classification output layer of the network to an output layer composed of two nodes, the values output by these two nodes are the identification points of the road marking graphics. Coordinates in the image frame. More specifically, the ResNet50 network can be used, and the final classification output layer is removed. According to the required recognition effect, two fully connected layers with 1024 nodes or 2048 nodes are used instead. After the fully connected layer, there are 2 connected In the output layer output by the node, the floating point data output by these two nodes belongs to the closed interval of [0, 1], and the pixel coordinates can be obtained by multiplying the output floating point data by the corresponding image width and height.
  • Deep neural network training step 106 The aforementioned training samples are input to the optimized deep neural network for training. After the training is completed, the deep neural network can be used to identify the road sign graphic and obtain the geometric center position coordinates.
  • FIG. 3 is a flowchart of a vehicle positioning method using the above deep neural network training method provided by the present invention, including a road sign pattern recognition step 201, a road sign pattern positioning step 202, and a vehicle positioning step 203.
  • Road sign pattern recognition step 201 Using the deep neural network after the training is completed, the road sign graphics photographed during the actual pit stop of the vehicle are recognized and the position coordinates (u, v) of the sign point P in the image coordinate system are obtained .
  • Road sign graphic positioning step 202 Calculate the coordinates (X w , Y w , Z w ) of the road sign graphic point P in the world coordinate system through the transformation relationship between the image coordinate system and the world coordinate system, thereby obtaining the road sign graphic The distance between the marking point P and the camera.
  • the transformation between the above image coordinate system and the world coordinate system can be described using a small hole imaging model.
  • Z C represents the horizontal distance between the road marking graphic marking point P and the optical center of the camera lens
  • d x , d y , u 0 , v 0 , f are the internal lens parameters related to the camera lens, specific for:
  • d x , d y represent the physical length of the unit pixel in the X and Y directions of the image coordinate system; u 0 , v 0 respectively represent the origin of the image coordinate system and the origin of the camera coordinate system in the X and Y directions Offset; f represents the lens imaging focal length.
  • R represents the rotation relationship between the world coordinate system and the camera coordinate system
  • formula (2) is used to calculate:
  • ⁇ , ⁇ , and ⁇ respectively represent the angles required to rotate around the X axis, Y axis, and Z axis when the world coordinate system and the camera coordinate system coincide.
  • T represents the translation relationship between the world coordinate system and the camera coordinate system
  • formula (3) is used to calculate:
  • t x , t y , and t z represent the translation amounts of the world coordinate system and the camera coordinate system on the X axis, Y axis, and Z axis, respectively.
  • the above parameters d x , d y , u 0 , v 0 , f, ⁇ , ⁇ , ⁇ , t x , t y , t z can be calibrated using but not limited to the conditions described below.
  • the shooting device is installed at the front roof of the vehicle and points in the forward direction.
  • the axis of the lens optical center of the shooting device coincides with the geometric symmetry centerline of the longitudinal axis of the vehicle.
  • the marking point P of the road marking on the road surface in front of the vehicle is on the axis of the lens optical center of the shooting device, and the horizontal distance from the lens optical center of the shooting device is Z C .
  • Set the origin of the camera coordinate system to the location of the imaging aperture of the camera.
  • the world coordinate system coincides with the camera coordinate system, and the forward direction of the vehicle is selected as the positive direction of the Z axis, the downward direction of the vehicle is the positive direction of the Y axis, and the right direction of the vehicle is the positive direction of the X axis.
  • the origin of the image coordinate system is on the Z axis of the camera coordinate system, and the X axis and Y axis of the image coordinate system are parallel to the X axis and Y axis of the camera coordinate system, respectively.
  • Vehicle positioning step 203 As shown in FIG. 6, after obtaining the horizontal distance Z C of the marking point P of the road marking graphic from the optical center of the lens of the shooting device, and then combining the distance L of the marking point P of the road marking graphic from the edge of the station in the direction of stop , You can calculate the horizontal distance L CZ of the optical center of the camera and the edge of the station in the direction of the station:
  • the distance between the vehicle and the edge of the station entering direction is determined, so as to realize vehicle positioning.

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Abstract

L'invention porte sur un procédé de positionnement de véhicule sur la base d'une reconnaissance d'image de réseau neuronal profond, et sur un procédé d'apprentissage pour un réseau neuronal profond. Le procédé d'apprentissage comprend : une configuration graphique de marquage de route (101), une configuration de dispositif de photographie (102), une collecte d'échantillon d'image (103), une réalisation d'échantillon d'apprentissage (104), une construction de réseau neuronal profond (105) et un apprentissage de réseau neuronal profond (106). Le processus de collecte d'échantillon d'image est réalisé pendant différentes périodes dans différentes conditions d'éclairage et de conditions météorologiques de telle sorte que l'adaptabilité environnementale du réseau neuronal profond est améliorée. De plus, en prenant des images d'échantillon chaque angle donné dans une direction de déplacement d'un véhicule et dans la direction perpendiculaire à la direction de déplacement du véhicule, une grande quantité de données d'échantillon d'apprentissage est obtenue, la précision d'apprentissage pour un réseau neuronal profond est améliorée et, donc, la précision du positionnement du véhicule est améliorée.
PCT/CN2019/111840 2018-10-24 2019-10-18 Procédé de positionnement de véhicule sur la base d'une reconnaissance d'image de réseau neuronal profond WO2020083103A1 (fr)

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