Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/60—Analysis of geometric attributes
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/70—Determining position or orientation of objects or cameras
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/12—Edge-based segmentation
• • 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
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
  • G06V10/761—Proximity, similarity or dissimilarity measures
• • 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
  • 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
  • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
  • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
  • G01S13/867—Combination of radar systems with cameras
• • 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
  • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• • 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
  • G06T2207/10—Image acquisition modality
  • G06T2207/10016—Video; Image sequence
  • G06T2207/10021—Stereoscopic video; Stereoscopic image sequence
• • 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
  • G06T2207/10—Image acquisition modality
  • G06T2207/10028—Range image; Depth image; 3D point clouds
• • 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
  • G06T2207/20084—Artificial neural networks [ANN]
• • 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
• • 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
  • G06V2201/07—Target detection

Definitions

• the invention relates to a method and a device for detecting and determining the height of objects in a vehicle environment.
• LIDAR systems and stereo camera systems are currently being developed for this task, as they offer the possibility of mapping the environment in 3D. In this way, the size and distance of the obstacles and the free space can be recorded at the same time.
• Stereo camera systems offer a significantly higher lateral resolution than LIDAR systems, while LIDAR systems allow very precise distance determination.
• LIDAR systems allow very precise distance determination.
• Previous stereo methods are mainly based on the SGM (Semi Global Matching) method.
• SGM Semi Global Matching
• machine learning methods based on deep neural networks, which are trained with ground truth data from LIDAR measurements.
• High-resolution LIDAR systems are still a factor of 3 to 6 above this value and are therefore insufficiently suitable for this task.
• High-resolution camera systems with 8 MPix image sensors and a lens with a 30° opening angle offer an angular resolution of 130 pixels/degree and display a 10 cm high object at a distance of 100 m on 8 pixels.
• a stereo camera system consisting of 2 high-resolution cameras could theoretically detect such a small object at a distance of 100m.
• the signal for noise in the depth map which is obtained from the stereo images, is usually too poor to unambiguously detect the object and at the same time to determine the height of the object.
• a method for detecting and determining the height of objects using an environment detection system comprising a first and a second environment detection sensor of a vehicle, with at least one of the environment detection sensors being a camera, is therefore proposed with the following steps:
• the camera is preferably a high-resolution telephoto camera.
• this camera has a resolution of 8 Mpix and an opening angle of 30° as well as an angular resolution of 130 pixels/degree.
• the second environment detection sensor is a stereo camera, for example.
• the environment detection system would be designed as a stereo camera and the mono camera could be a component of the stereo system in this embodiment. It would also be conceivable to use a single mono camera and a separate stereo camera.
• the environment representation of the second environment detection sensor would be a depth image. It would also be conceivable to use a radar or lidar sensor as the second environment detection sensor.
• the area representation In the case of a radar sensor, the area representation would be an object list or a radar signature list, in which detected objects or radar signatures and their distances from the vehicle are entered. This data can then be merged with the mono image.
• the environment representation With a lidar sensor as the second environment detection sensor, the environment representation would be a point cloud.
• the singular number is used for the object for the sake of readability. However, it goes without saying that a number of objects can also be detected.
• the two steps for object detection can take place simultaneously or sequentially in any order. For example, the object can be detected first in the environment representation of the second environment detection sensor and only then can the object be detected in the mono image. As already mentioned, a reverse order or a simultaneous process would also be conceivable.
• the detection of the object in the mono image indicates the size of the object in pixels.
• the distance to the object is preferably measured by means of the second environment detection sensor, in particular if this is a radar or lidar sensor.
• the height of the object is based on a height of the object in pixels in the mono image, the determined distance of the object and a known angular resolution of the Environment detection sensors determined.
• the angular resolution of the respective sensors results from the specific sensor data.
• the object detection in the mono image is carried out using semantic segmentation based on a trained convolutional neural network CNN.
• a trained convolutional neural network CNN This is advantageous since objects can be detected efficiently by means of the semantic segmentation and not only the detection of objects can be improved by means of a correspondingly trained CNN, but also the shape of the object can be determined. This information can be helpful in determining whether an object would be traversable, if necessary, without damaging the vehicle.
• the object is confirmed by comparing the mono image and the second environment representation. In this embodiment, a comparison is made as to whether the positions of the detections match. If there is a match in the comparison, the object is unequivocally confirmed.
• the object detection of one of the surroundings detection sensors defines an area of interest for the other surroundings detection sensor.
• the object detection of the second environment detection sensor for example by means of a radar sensor, defines a region of interest (ROI) or an area of interest or also a search area for the camera. It is thus achieved that only this specific area in the mono image is examined for an object. This leads to more efficient object detection. It would also be conceivable that the object detection in the mono image would allow an increase in sensitivity in the radar search field. As a result, very weak radar detections, for example, can be recognized as objects. Here, too, this leads to more efficient and, above all, more reliable object detection.
• an environment detection system for a vehicle comprising a first and a second environment detection sensor with a specific angular resolution and a computing unit, with at least the first environment detection sensor being designed as a camera, with a mono image being recorded with the camera and a further environment representation being recorded with the second environment detection sensor, with the processing unit being configured to record an object in the mono image and in the environment representation of the second environment detection sensor, the computing unit being further configured to determine the distance and the height of the object.
• the first surroundings detection sensor is a telecamera and the second surroundings detection sensor is a stereo camera, a radar sensor or a lidar sensor.
• the second environment detection sensor is designed as a stereo camera, with the telecamera being a component of the stereo camera.
• the second environment detection sensor would thus be the stereo camera and the first would still be a mono camera.
• the stereo camera serves as a height-determining sensor by determining the height in a depth image.
• FIG. 2 shows a schematic representation of a surroundings detection system according to an embodiment of the invention
• FIG. 4 shows an example of a depth image from the scene shown in FIG. 3 .
• FIG. 1 shows a schematic flowchart of a preferred embodiment of the method for detecting and determining the height of objects using a stereo detection system, comprising a first and a second environment detection sensor of a vehicle, with at least one of the environment detection sensors being a mono camera.
• a mono image is recorded by the camera.
• an environment representation is recorded with the second environment detection sensor.
• an object detection is carried out in the mono image.
• an object is detected in the environment representation of the second environment detection sensor.
• Steps S3 and S4 can be carried out one after the other in any order, ie S4 before S3 or S3 before S4, or at the same time.
• step S5 the distance to the object is determined in the environment representation of the second environment detection sensor.
• step S6 the height of the detected object is determined.
• FIG. 2 shows a schematic representation of a surroundings detection system 1 according to an embodiment of the invention.
• the environment detection system 1 includes a first environment detection sensor 2a, which is preferably designed as a camera, and a second environment detection sensor 2b. These two surroundings detection sensors 2a, 2b are connected to a computing unit 3 via a data connection D.
• This processing unit 3 is designed to detect an object in the mono image, the processing unit being further designed to carry out object detection in the mono image and the environment representation of the second environment detection sensor, and the processing unit being further designed to determine the distance and height of the object.
• the data connection D can be wired or wireless.
• FIG. 3 shows a representation of an exemplary scene.
• This representation shows a section of a roadway.
• the representation here is mono image M.
• the objects 01, 02, 03 are detected in the mono image M, for example by means of a semantic segmentation.
• FIG. 4 shows an exemplary representation of a depth image recorded by means of a stereo camera from the scene shown in FIG.
• the second environment detection sensor is designed as a stereo camera.
• the objects 01, 02 and 03 are visible as elevations.
• the distance to the objects 01, 02, 03 can also be determined in the depth image T.

Landscapes

• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Radar, Positioning & Navigation (AREA)
• Remote Sensing (AREA)
• Theoretical Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Multimedia (AREA)
• Computer Networks & Wireless Communication (AREA)
• Geometry (AREA)
• Electromagnetism (AREA)
• Artificial Intelligence (AREA)
• Health & Medical Sciences (AREA)
• Computing Systems (AREA)
• Databases & Information Systems (AREA)
• Evolutionary Computation (AREA)
• General Health & Medical Sciences (AREA)
• Medical Informatics (AREA)
• Software Systems (AREA)
• Image Processing (AREA)
• Length Measuring Devices By Optical Means (AREA)
• Measurement Of Optical Distance (AREA)
• Optical Radar Systems And Details Thereof (AREA)
• Image Analysis (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=80684868

Family Applications (1)

Country Status (7)

Family Cites Families (6)

• 2021
  • 2021-02-24 DE DE102021201713.4A patent/DE102021201713A1/de active Pending
• 2022
  • 2022-01-27 US US18/547,028 patent/US20240135720A1/en active Pending
  • 2022-01-27 CN CN202280013045.5A patent/CN116888630A/zh active Pending
  • 2022-01-27 EP EP22709172.5A patent/EP4298600A1/de active Pending
  • 2022-01-27 JP JP2023542755A patent/JP2024505422A/ja active Pending
  • 2022-01-27 WO PCT/DE2022/200009 patent/WO2022179669A1/de active Application Filing
  • 2022-01-27 KR KR1020237023997A patent/KR20230118974A/ko unknown

Also Published As

Similar Documents

Legal Events