CN111025241A - Boundary area detection method and device, electronic equipment and storage medium - Google Patents
Boundary area detection method and device, electronic equipment and storage medium Download PDFInfo
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- CN111025241A CN111025241A CN201910989747.7A CN201910989747A CN111025241A CN 111025241 A CN111025241 A CN 111025241A CN 201910989747 A CN201910989747 A CN 201910989747A CN 111025241 A CN111025241 A CN 111025241A
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- 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
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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Abstract
The application relates to a boundary area detection method, a boundary area detection device, an electronic device and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining point cloud data according to received echo signals, analyzing the point cloud data to determine a target object of a preset type, obtaining target data points corresponding to the target object, and determining a boundary area according to the target data points. According to the technical scheme, the point cloud data of different objects in the preset range are obtained by processing the echo signals, the obtained point cloud data are analyzed, the target object is determined, and the three-dimensional coordinates of the target object are fitted, so that the boundary of the preset range is obtained, the self-adaptive boundary area detection can be realized, and the detection precision is improved.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a method and an apparatus for detecting a boundary area, an electronic device, and a storage medium.
Background
With the development of the internet, the application of the environment sensing technology in the life of people is more and more extensive, for example: domestic intelligent lamp can regional perception, and domestic air conditioner can carry out the scope and detect, and common environmental perception technique has modes such as infrared ray, brain wave, vision processing, but above-mentioned means easily receives external illumination and temperature factor to influence, and detection distance information error often appears, has the detection deviation scheduling problem, so can lead to the boundary inaccuracy of final generation. There is a need for a more accurate way to detect boundaries.
Disclosure of Invention
In order to solve the technical problem or at least partially solve the technical problem, the present application provides a boundary area detection method, an apparatus, an electronic device and a storage medium.
In a first aspect, the present application provides a boundary area detection method, including:
acquiring point cloud data according to the received echo signals;
analyzing the point cloud data to determine a target object of a preset type;
acquiring a target data point corresponding to the target object;
a bounding region is determined from the target data points.
In one possible embodiment, the acquiring point cloud data according to the received echo signals includes:
converting the echo signal into a digital signal;
analyzing the digital signal to obtain frequency spectrum information;
obtaining the point cloud data according to the frequency spectrum information;
wherein the point cloud data comprises at least one of: distance, azimuth, elevation, and/or radial velocity.
In one possible embodiment, the analyzing the point cloud data to determine a preset type of target object includes:
determining a data point set according to the point cloud data;
acquiring data points which accord with preset conditions from the data point set;
clustering the data points which accord with the preset conditions to obtain a clustering object;
wherein the data points meeting the preset condition include: the data point where the radial velocity is 0 and the distance at the same azimuth angle has the largest value.
In one possible embodiment, the analyzing the point cloud data to determine a preset type of target object further includes:
acquiring characteristic information of the clustering object according to the point cloud data;
selecting a clustering object which accords with the preset type as a target object based on the characteristic information;
wherein the characteristic information includes at least one of: torso bandwidth, centroid, total bandwidth, frequency, period, or offset.
In a possible embodiment, the selecting, as the target object, the clustering object that conforms to the preset type based on the feature information includes:
inputting the characteristic information into a trained classification model for classification to obtain a preset type of clustering object;
and taking a preset type of clustering object as the target object.
In one possible embodiment, the method further comprises:
acquiring training sample data, wherein the training sample data at least comprises a preset type of clustering object;
acquiring marking information in the training sample data, wherein the marking information comprises type labels corresponding to all clustering objects in the training sample data;
and training the training sample data and the type label by adopting a preset convolutional neural network to obtain the classification model.
In one possible embodiment, the method further comprises:
projecting the target data points to a two-dimensional plane to obtain a boundary line set;
and fitting each boundary line in the boundary line set by adopting a least square method to generate the boundary area graph.
In a second aspect, the present application provides a boundary area detection apparatus, the apparatus comprising:
the acquisition module is used for acquiring point cloud data according to the received echo signals;
the analysis module is used for analyzing the point cloud data to determine a target object of a preset type;
acquiring a target data point corresponding to the target object;
a determining module for determining a boundary region according to the target data point.
In a third aspect, the present application provides an electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
the memory is used for storing a computer program;
the processor is configured to implement the above method steps when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the above-mentioned method steps.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: according to the method and the device, the point cloud data of different objects in the preset range are obtained through processing the echo signals, the obtained point cloud data are analyzed, the target object is determined, and the three-dimensional coordinates of the target object are fitted, so that the boundary of the preset range is obtained, the self-adaptive boundary area detection can be realized, and the detection precision is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a boundary area detection method according to an embodiment of the present disclosure;
fig. 2 is a schematic spatial diagram of radar monitoring provided in an embodiment of the present application;
fig. 3 is a flowchart of a boundary area detection method according to another embodiment of the present application;
fig. 4 is a block diagram of a boundary area detection apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The method provided by the embodiment of the invention can be applied to any required electronic equipment, such as electronic equipment such as a server and a terminal, and is not particularly limited herein, and for convenience of description, the method is hereinafter simply referred to as electronic equipment. First, a boundary detection method provided in an embodiment of the present invention is described below.
Fig. 1 is a flowchart of a boundary area detection method according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
step S11, point cloud data in a preset range are obtained according to the received echo signals;
step S12, analyzing the point cloud data to determine a preset type of target object;
step S13, acquiring a three-dimensional target data point of a target object;
in step S14, a boundary region is determined based on the target data point.
According to the method and the device, the millimeter wave radar signals reflected by the acquired scene are analyzed, the radar signals are processed to obtain point cloud data information of different objects in the specific scene, the point cloud data are analyzed to obtain the target object, and therefore the position of the boundary area in the scene is obtained, and self-adaptive boundary area detection is achieved.
Fig. 2 is a schematic spatial diagram of radar monitoring provided in an embodiment of the present application, and as shown in fig. 2, the millimeter wave radar may scan a rectangular area in front of the millimeter wave radar, where a blind area is located near the radar, that is, an area surrounded by a dotted line in the figure. And then, transmitting an electromagnetic wave radar signal to a room through the millimeter wave radar chip, and feeding back an echo signal to the radar after the electromagnetic wave meets an object in the space.
In this embodiment, after the echo signal is received, the echo signal is converted into a digital signal, spectrum analysis is performed using fourier transform to obtain spectrum information, and a distance, an azimuth angle, and an elevation angle of an object in a preset range with respect to the radar device are obtained according to the spectrum information. And calculating the radial velocity of the data point according to the phase difference of the two adjacent frames of data by using the Doppler effect principle, thereby obtaining point cloud data after a series of processing is carried out on the echo signal, and obtaining a data point set based on the point cloud data.
In this embodiment, the boundary region is detected, so that data points satisfying the preset condition are obtained from the data point set, where the preset condition is that the data point has a radial velocity of 0 and the distance at the same azimuth is the largest, and specifically, in this embodiment, the data points having a radial velocity greater than 0 are filtered by establishing a doppler filter. And then establishing a distance filter, and filtering the data points smaller than the maximum distance again through the distance filter to obtain the data points meeting the preset conditions.
Then, clustering the data points meeting the preset conditions to obtain a clustering object, for example: taking a living room as an example, the clustering object can be a television, an air conditioner, a sofa, a bookcase or a wall and the like. Acquiring characteristic information of the clustering object according to the point cloud data, and selecting the clustering object which is in accordance with a preset type as a target object according to the characteristic information, wherein the characteristic information at least comprises one of the following items: torso bandwidth, centroid, total bandwidth, frequency, period, or offset.
And selecting the clustering object which accords with the preset type as a target object according to the characteristic information, inputting the characteristic information into a trained classification model for classification to obtain the clustering object of the preset type, and taking the clustering object of the preset type as the target object. The target object in this embodiment is a wall and a wall-like body, where the wall-like body means that an object is placed beside the wall to shield the wall, and at this time, the object is used as the wall-like body, for example, in a living room, a general refrigerator is placed close to the wall, and at this time, the refrigerator is used as the wall-like body. And then determining a boundary area according to the wall body and the wall-like body. Specifically, a target data point corresponding to the target object, that is, a wall and a wall-like body, is obtained, and the boundary area may be determined according to the coordinate information of the target data point.
The deep learning model adopted in the embodiment is a Pointnet + + network model, and the training process of the Pointnet + + network model is as follows: acquiring training sample data, wherein the training sample data at least comprises preset type clustering objects, and acquiring labeling information in the training sample data, wherein the labeling information comprises type labels corresponding to the clustering objects in the training sample data, such as: when the application scene is a living room, the type label of the refrigerator is a wall-like body, the type label of the tea table is a non-wall body, the type label of the bearing wall is a wall body, and then the training sample data and the type label are trained by adopting a preset convolutional neural network, so that a trained Pointernet + + network model is obtained.
Fig. 3 is a flowchart of a boundary area detection method according to another embodiment of the present application, and as shown in fig. 3, the method further includes the following steps:
step S31, projecting the target data point to a two-dimensional plane to obtain a boundary line set;
and step S32, fitting each boundary line in the boundary line set by adopting a least square method to generate a boundary region graph.
In practice, the millimeter wave radar cannot detect all the boundary walls, so the detected walls are not continuous, and the detected discrete boundary lines need to be fitted into a whole.
In this embodiment, the target data points corresponding to the target object are projected on a two-dimensional plane, since the wall and the wall-like body are perpendicular to the ground, a boundary line set is formed by projecting the corresponding data points on the two-dimensional plane, and then each boundary line in the boundary line set is fitted by using a least square method to generate a boundary area graph.
Specifically, a plurality of boundary lines in a discrete state in the same direction and/or with a direction difference smaller than a preset threshold value are fitted. Or, if the direction difference between the projection of the data point corresponding to the wall-like body or the wall in a certain direction and the direction is greater than the preset threshold, a boundary needs to be fitted again. The finally generated border area pattern may be a pattern of any shape, such as a rectangle, a polygon, or a circle.
Fig. 4 is a block diagram of a boundary area detection apparatus provided in an embodiment of the present application, which may be implemented as part or all of an electronic device through software, hardware, or a combination of the two. As shown in fig. 4, the boundary area detection apparatus includes:
a processing module 401, configured to obtain point cloud data according to the received echo signal;
an analysis module 402, configured to analyze the point cloud data to determine a target object of a preset type;
an obtaining module 403, configured to obtain a target data point corresponding to a target object;
a determining module 404 for determining a boundary region from the target data point.
An embodiment of the present application further provides an electronic device, as shown in fig. 5, the electronic device may include: the system comprises a processor 1501, a communication interface 1502, a memory 1503 and a communication bus 1504, wherein the processor 1501, the communication interface 1502 and the memory 1503 complete communication with each other through the communication bus 1504.
A memory 1503 for storing a computer program;
the processor 1501 is configured to implement the steps of the above embodiments when executing the computer program stored in the memory 1503.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring point cloud data according to the received echo signals;
analyzing the point cloud data to determine a target object of a preset type;
acquiring a target data point corresponding to the target object;
a bounding region is determined from the target data points.
Optionally, the computer program, when executed by the processor, further implements the steps of:
acquiring point cloud data according to the received echo signals, comprising:
converting the echo signal into a digital signal;
analyzing the digital signal to obtain frequency spectrum information;
point cloud data are obtained according to the frequency spectrum information;
wherein the point cloud data comprises at least one of: distance, azimuth, elevation, and/or radial velocity.
Optionally, the computer program, when executed by the processor, further implements the steps of:
analyzing the point cloud data to determine a preset type of target object, including:
determining a data point set according to the point cloud data;
acquiring data points which accord with preset conditions from the data point set;
clustering data points which meet preset conditions to obtain a clustering object in a preset range;
wherein, the data points which accord with the preset condition comprise: the data point where the radial velocity is 0 and the distance at the same azimuth angle has the largest value.
Optionally, the computer program, when executed by the processor, further implements the steps of:
analyzing the point cloud data to determine a preset type of target object, and further comprising:
acquiring characteristic information of the clustering object according to the point cloud data;
selecting a clustering object which accords with a preset type as a target object based on the characteristic information;
wherein the characteristic information at least comprises one of the following items: torso bandwidth, centroid, total bandwidth, frequency, period, or offset.
Optionally, the computer program, when executed by the processor, further implements the steps of: selecting a clustering object which accords with a preset type as a target object based on the characteristic information, wherein the method comprises the following steps:
inputting the characteristic information into a trained classification model for classification to obtain a preset type of clustering object;
and taking the clustering object of the preset type as a target object.
Optionally, the computer program, when executed by the processor, further implements the steps of:
acquiring training sample data, wherein the training sample data at least comprises a preset type of clustering object;
acquiring marking information in training sample data, wherein the marking information comprises type labels corresponding to all clustering objects in the training sample data;
and training the training sample data and the type label by adopting a preset convolutional neural network to obtain a classification model.
Optionally, the computer program, when executed by the processor, further implements the steps of:
projecting the target data point to a two-dimensional plane to obtain a boundary line set;
and fitting each boundary line in the boundary line set by adopting a least square method to generate a boundary area graph.
For the readable storage medium embodiment, since it is basically similar to the method embodiment, the description is simple, and for relevant points, refer to part of the description of the method embodiment.
It is further noted that, herein, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for detecting a boundary region, comprising:
acquiring point cloud data according to the received echo signals;
analyzing the point cloud data to determine a target object of a preset type;
acquiring a target data point corresponding to the target object;
a bounding region is determined from the target data points.
2. The method of claim 1, wherein the acquiring point cloud data from the received echo signals comprises:
converting the echo signal into a digital signal;
analyzing the digital signal to obtain frequency spectrum information;
obtaining the point cloud data according to the frequency spectrum information;
wherein the point cloud data comprises at least one of: distance, azimuth, elevation, and/or radial velocity.
3. The method of claim 2, wherein analyzing the point cloud data to determine a preset type of target object comprises:
determining a data point set according to the point cloud data;
acquiring data points which accord with preset conditions from the data point set;
clustering the data points which accord with the preset conditions to obtain a clustering object;
wherein the data points meeting the preset condition include: the data point where the radial velocity is 0 and the distance at the same azimuth angle has the largest value.
4. The method of claim 3, wherein the analyzing the point cloud data to determine a preset type of target object further comprises:
acquiring characteristic information of the clustering object according to the point cloud data;
selecting a clustering object which accords with the preset type as a target object based on the characteristic information;
wherein the characteristic information includes at least one of: torso bandwidth, centroid, total bandwidth, frequency, period, or offset.
5. The method according to claim 4, wherein the selecting, as a target object, a cluster object conforming to the preset type based on the feature information comprises:
inputting the characteristic information into a trained classification model for classification to obtain a preset type of clustering object;
and taking a preset type of clustering object as the target object.
6. The method of claim 5, further comprising:
acquiring training sample data, wherein the training sample data at least comprises a preset type of clustering object;
acquiring marking information in the training sample data, wherein the marking information comprises type labels corresponding to all clustering objects in the training sample data;
and training the training sample data and the type label by adopting a preset convolutional neural network to obtain the classification model.
7. The method of claim 1, further comprising:
projecting the target data points to a two-dimensional plane to obtain a boundary line set;
and fitting each boundary line in the boundary line set by adopting a least square method to generate a boundary area graph.
8. A boundary area detection apparatus, comprising:
the acquisition module is used for acquiring point cloud data according to the received echo signals;
the analysis module is used for analyzing the point cloud data to determine a target object of a preset type;
acquiring a target data point corresponding to the target object;
a determining module for determining a boundary region according to the target data point.
9. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
the memory is used for storing a computer program;
the processor, when executing the computer program, implementing the method steps of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
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