WO2020259284A1 - Procédé et dispositif de détection d'obstacle - Google Patents

Procédé et dispositif de détection d'obstacle Download PDF

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Publication number
WO2020259284A1
WO2020259284A1 PCT/CN2020/095278 CN2020095278W WO2020259284A1 WO 2020259284 A1 WO2020259284 A1 WO 2020259284A1 CN 2020095278 W CN2020095278 W CN 2020095278W WO 2020259284 A1 WO2020259284 A1 WO 2020259284A1
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WO
WIPO (PCT)
Prior art keywords
obstacle
road condition
image
information
roi
Prior art date
Application number
PCT/CN2020/095278
Other languages
English (en)
Chinese (zh)
Inventor
谢伟伦
刘兴业
Original Assignee
华为技术有限公司
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 华为技术有限公司 filed Critical 华为技术有限公司
Publication of WO2020259284A1 publication Critical patent/WO2020259284A1/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
    • 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
    • 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
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • 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
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • 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
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds

Definitions

  • the present application provides an obstacle detection method and device to expand the detection range of obstacle detection, thereby improving the accuracy of obstacle detection.
  • the first device can obtain at least one first obstacle and at least one second obstacle, thereby expanding the detection range of obstacle detection, which can detect common obstacles. It can also detect uncommon obstacles, thereby improving the accuracy of obstacle detection and facilitating the design of subsequent obstacle avoidance control algorithms. Further, since the detection of obstacles in the road condition image is pixel-level detection, the obtained descriptions of the first obstacle and the second obstacle are also pixel-level descriptions, so compared to other obstacle detection methods, the detection range is wider .
  • the first device detects the first part of the road condition image by the second image recognition method, including: the first device determines the region of interest (ROI) in the first part ; The first device detects the ROI through the second image recognition method.
  • ROI region of interest
  • the above-mentioned ROI may be used as a detection area of the second-level image recognition, so that the first device can obtain at least one second obstacle by detecting the ROI.
  • the ROI includes an upper boundary, a lower boundary, a left boundary, and/or a right boundary; accordingly, the above method further includes: the first device acquires scene information, the scene information corresponding to the current driving Scene; then, the first device obtains the ROI from the first part, including at least one of the following: the first device scans the pixels of the first part line by line to determine the upper boundary of the ROI; the first device determines the ROI according to the scene information Lower boundary; the first device scans the pixels of the first part column by column to determine the left boundary and/or right boundary of the ROI.
  • anomaly detection algorithms may include spatiotemporal autoencoder-based detection algorithms, saliency detection algorithms, and the like.
  • the first device combines the results of the first-level image recognition and the results of the second-level image recognition to jointly determine the obstacles in the road condition image, and remove at least one first obstacle from the at least one second obstacle Part or all of the, so as to determine the obstacles not detected by the first-level image recognition.
  • the ROI includes an upper boundary, a lower boundary, a left boundary, and/or a right boundary; the processing module is also used to obtain scene information, which corresponds to the current driving scene; and, processing The module is specifically used to perform one or more of the following: scan the pixels of the first part line by line to determine the upper boundary of the ROI; determine the lower boundary of the ROI according to the scene information; scan the pixels of the first part column by column , Determine the left boundary and/or right boundary of the ROI.
  • the camera device is a monocular camera
  • the scene information includes speed parameters and/or shooting parameters of the monocular camera.
  • FIG. 4 is a schematic diagram of the boundary of the ROI in an embodiment of the application.
  • the power supply 101 may be configured to provide power to some or all of the components of the vehicle 100.
  • the power source 110 may include, for example, a rechargeable lithium ion or lead-acid battery.
  • one or more battery packs may be configured to provide power.
  • Other power supply materials and configurations are also possible.
  • the power supply 110 and the energy source 113 may be implemented together.
  • the second image recognition method is a saliency detection algorithm.
  • the training data can be calibrated according to the foreground and background, and the calibration data can be learned in a supervised learning manner to obtain a saliency detection model.
  • the first device inputs the road surface data with obstacles, that is, the first part of the road condition image into the saliency detection model, which can calibrate the obstacles in the first part in the foreground. Calibrate in the background to determine the position of the second obstacle.
  • the first device may extract the ROI by determining the boundary of the ROI.
  • the ROI may include an upper boundary, a lower boundary, a left boundary, and/or a right boundary.
  • the first device may also output the obstacle information to the second device according to the correspondence between the second device and the obstacle information.
  • FIG. 6 is a schematic structural diagram of the computing device in an embodiment of the application.
  • the computing device 600 may include: processing The processor 601 and the communication interface 602, and the processor 601 may be used to support the computing device 600 to implement the functions involved in the foregoing embodiments.
  • the processor 601 may obtain the road condition images collected by the camera device through the communication interface 602.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé et un dispositif de détection d'obstacle, apte à être appliqué à une caméra embarquée ou à un dispositif de capture d'image embarqué pour étendre la plage de détection de la détection d'obstacle de façon à améliorer la précision de la détection d'obstacle, et se rapportant au domaine de la conduite automatique ou de la conduite intelligente. Le procédé peut comprendre les étapes suivantes : un premier dispositif obtient une image de l'état de la route au moyen d'un dispositif de capture d'image (S201) ; le premier dispositif détermine, en fonction de l'image de l'état de la route, au moins un premier obstacle et une région pouvant être parcourue au moyen d'un premier procédé de reconnaissance d'image (S202), la région pouvant être parcourue correspondant à une première partie de l'image de l'état de la route ; et le premier dispositif détermine, en fonction de la première partie de l'image de l'état de la route, au moins un second obstacle au moyen d'un second procédé de reconnaissance d'image. Le procédé est utilisé pour la détection d'obstacles lors d'une conduite auxiliaire ou d'une conduite automatique.
PCT/CN2020/095278 2019-06-27 2020-06-10 Procédé et dispositif de détection d'obstacle WO2020259284A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910567635.2 2019-06-27
CN201910567635.2A CN112149460A (zh) 2019-06-27 2019-06-27 一种障碍物检测方法及装置

Publications (1)

Publication Number Publication Date
WO2020259284A1 true WO2020259284A1 (fr) 2020-12-30

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PCT/CN2020/095278 WO2020259284A1 (fr) 2019-06-27 2020-06-10 Procédé et dispositif de détection d'obstacle

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CN (1) CN112149460A (fr)
WO (1) WO2020259284A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115147838A (zh) * 2022-06-30 2022-10-04 小米汽车科技有限公司 图像处理方法、装置、车辆、介质及程序产品

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112706764A (zh) * 2020-12-30 2021-04-27 潍柴动力股份有限公司 一种主动防碰撞预警方法、装置、设备和存储介质
CN112793567A (zh) * 2021-01-14 2021-05-14 史鹏飞 一种基于路况检测的辅助驾驶方法及系统
TWI766560B (zh) * 2021-01-27 2022-06-01 國立臺灣大學 結合語義分割與光達點雲之物件辨識與測距系統
CN113011255B (zh) * 2021-02-05 2024-01-16 北京中科慧眼科技有限公司 基于rgb图像的路面检测方法、系统和智能终端
CN114155447B (zh) * 2021-12-02 2022-06-24 北京中科智易科技有限公司 人工智能大数据采集系统
CN116612194B (zh) * 2023-07-20 2023-10-20 天津所托瑞安汽车科技有限公司 一种位置关系确定方法、装置、设备及存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105216797A (zh) * 2015-08-21 2016-01-06 奇瑞汽车股份有限公司 超车方法及系统
US20170060132A1 (en) * 2015-08-31 2017-03-02 Korea University Research And Business Foundation Method for detecting floor obstacle using laser range finder
CN107563256A (zh) * 2016-06-30 2018-01-09 北京旷视科技有限公司 辅助驾驶信息产生方法及装置、辅助驾驶系统
CN107953827A (zh) * 2016-10-18 2018-04-24 杭州海康威视数字技术股份有限公司 一种车辆盲区预警方法及装置
CN109070882A (zh) * 2016-11-28 2018-12-21 忠北国立大学产学合作基金会 利用摄像头影像的行驶信息提供方法及装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105216797A (zh) * 2015-08-21 2016-01-06 奇瑞汽车股份有限公司 超车方法及系统
US20170060132A1 (en) * 2015-08-31 2017-03-02 Korea University Research And Business Foundation Method for detecting floor obstacle using laser range finder
CN107563256A (zh) * 2016-06-30 2018-01-09 北京旷视科技有限公司 辅助驾驶信息产生方法及装置、辅助驾驶系统
CN107953827A (zh) * 2016-10-18 2018-04-24 杭州海康威视数字技术股份有限公司 一种车辆盲区预警方法及装置
CN109070882A (zh) * 2016-11-28 2018-12-21 忠北国立大学产学合作基金会 利用摄像头影像的行驶信息提供方法及装置

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115147838A (zh) * 2022-06-30 2022-10-04 小米汽车科技有限公司 图像处理方法、装置、车辆、介质及程序产品
CN115147838B (zh) * 2022-06-30 2023-08-29 小米汽车科技有限公司 图像处理方法、装置、车辆、介质及程序产品

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