CN113627277A - Method and device for identifying parking space - Google Patents
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Abstract
The disclosure relates to a method and a device for identifying parking spaces. The method comprises the following steps: acquiring an angular point set and a line segment set of a parking space in a panoramic image; acquiring angular points of which the interval distances are within a preset range from the angular point set, and taking the angular points as angular points of the target parking space; acquiring line segments passing through the corner points from the line segment set; and predicting residual angular points according to the angular points and the line segments, and determining the target parking space. And under the condition that the information of the target parking space in the panoramic image is not much, namely the information comprises partial angular points or parking space lines of the parking space, predicting the residual angular points through the acquired angular point set and line segment set, thereby determining the target parking space and accurately identifying the target parking space.
Description
Technical Field
The disclosure relates to the technical field of automatic driving, in particular to a method and a device for identifying parking places.
Background
With the development of economy, the occupancy of vehicles has increased year by year, and the problem of parking in a limited urban space has become increasingly troublesome. The parking space is insufficient and narrow, which undoubtedly brings great trouble to the driver. Resulting in a great deal of time spent in parking; or the parking space can not be accurately parked for multiple times in the parking process; or rubbing between the parking space and the adjacent parking spaces or wall bodies. In the related art, an automatic parking system or a guest parking system has appeared, which can realize automatic parking of a vehicle. However, in the above system, since the visual sensor of the vehicle can only capture part of the corner points or the parking space lines of the parking space, the complete parking space position cannot be accurately estimated, and the accuracy of parking space identification is not high.
Therefore, a method and a device capable of accurately identifying parking spaces are needed.
Disclosure of Invention
To overcome at least one of the problems in the related art, the present disclosure provides a method and apparatus for detecting a parking space.
According to a first aspect of the embodiments of the present disclosure, a method for identifying a parking space is provided, including:
acquiring an angular point set and a line segment set of a parking space in a panoramic image;
acquiring angular points of which the interval distances are within a preset range from the angular point set, and taking the angular points as angular points of the target parking space;
acquiring line segments passing through the corner points from the line segment set;
and predicting residual angular points according to the angular points and the line segments, and determining the target parking space.
In a possible implementation manner, the predicting remaining angular points according to the angular points and the line segments and determining the target parking space includes:
acquiring a first line segment passing through only one corner point from the line segments;
the angular points are used as end points, the first line segment is lengthened or shortened to a preset length, and the other end point of the first line segment is used as a residual angular point of the target parking space;
and determining the target parking space according to the angular points and the residual angular points.
In one possible implementation, the preset length is set to be obtained as follows:
and converting the parking space side length into a coordinate length in the image according to the proportional relation between the parking space side length and the image coordinate.
In a possible implementation manner, the obtaining, from the line segment set, a line segment passing through the corner point includes:
establishing an image coordinate system, and determining a linear expression of the line segments in the line segment set;
substituting the angular point into the linear expression, and if the angular point meets the linear expression of the line segment, enabling the line segment to pass through the angular point.
In a possible implementation manner, the acquiring a set of corner points in the panoramic image includes:
acquiring a panoramic image within a preset range of a vehicle;
inputting the panoramic image into a key point detection model, and outputting the corner position of the parking space in the panoramic image through the key point detection model, wherein the key point detection model is set to be obtained by training by utilizing the corresponding relation between the sample panoramic image and the sample key points.
In a possible implementation manner, the line segment set of the parking space in the panoramic image is obtained. The method comprises the following steps:
acquiring a panoramic image within a preset range of a vehicle;
and inputting the panoramic image into an image segmentation model, and outputting the line segment of the parking space in the panoramic image through the image segmentation model, wherein the image segmentation model is set to be obtained by utilizing the corresponding relation between the sample panoramic image and the sample line segment for training.
According to a second aspect of the embodiments of the present disclosure, there is provided a device for identifying a parking space, including:
the first acquisition module is used for acquiring an angular point set and a line segment set of a parking space in the panoramic image;
the second acquisition module is used for acquiring angular points of which the interval distances are within a preset range from the angular point set, and taking the angular points as angular points of a target parking space;
a third obtaining module, configured to obtain a line segment passing through the corner point from the line segment set;
and the determining module is used for predicting the residual angular points according to the angular points and the line segments and determining the target parking space.
In one possible implementation, the determining module includes:
the obtaining submodule is used for obtaining a first line segment which only passes through one corner point from the line segments;
the adjusting submodule is used for prolonging or shortening the first line segment to a preset length by taking the angular point as an end point, and taking the other end point of the first line segment as a residual angular point of the target parking space;
and the determining submodule is used for determining the target parking space according to the angular point and the residual angular point.
According to a third aspect of the embodiments of the present disclosure, there is provided a device for identifying a parking space, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method according to any one of the embodiments of the present disclosure.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having instructions which, when executed by a processor, enable the processor to perform the method of any one of claims 1 to 6.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: and under the condition that the information of the target parking space in the panoramic image is not much, namely the information comprises partial angular points or parking space lines of the parking space, predicting the residual angular points through the acquired angular point set and line segment set, thereby determining the target parking space and accurately identifying the target parking space.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart illustrating a method for identifying a space in accordance with an exemplary embodiment.
Fig. 2 is a schematic diagram illustrating a corner point type according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating a method of identifying a space in accordance with an exemplary embodiment.
Fig. 4 is a schematic block diagram of an apparatus for identifying a space according to an exemplary embodiment.
Fig. 5 is a schematic block diagram of an apparatus for identifying a space according to an exemplary embodiment. .
Fig. 6 is a schematic block diagram of an apparatus for identifying a parking space according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The method for identifying parking spaces according to the present disclosure is described in detail below with reference to fig. 1. Fig. 1 is a flowchart of a method of an embodiment of a method for identifying a parking space provided by the present disclosure. Although the present disclosure provides method steps as illustrated in the following examples or figures, more or fewer steps may be included in the method based on conventional or non-inventive efforts. In steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by the disclosed embodiments.
Specifically, one embodiment of the method for identifying a parking space provided by the present disclosure is shown in fig. 1, and the method may be applied to a vehicle, and includes:
and S101, acquiring an angular point set and a line segment set of the parking space in the panoramic image.
In the embodiment of the present disclosure, the panoramic image may be captured by a 360-degree panoramic camera in one example, and the 360-degree panoramic camera may image 360-degree horizontal and 180-degree vertical information at a time by using a principle of transmission and reflection of a spherical mirror in physical optics. In another example, the panoramic image may also be obtained by stitching images taken at the same time by a plurality of cameras installed at different locations of the vehicle. The set of angular points includes all the angular points identified in the panoramic image, and the angular points include two or more than two angular points, such as right angles of a rectangular parking space, acute angles or obtuse angles of a parallelogram. The line segment set comprises all line segments identified in the panoramic image, and the line segments comprise line segments forming a parking space line, such as right-angle edges of a rectangular parking space, bottom edges or oblique edges of a parallelogram and the like.
Fig. 2 is a schematic diagram illustrating a corner point type according to an exemplary embodiment. As shown with reference to fig. 2. In an embodiment of the present disclosure, a method for acquiring a corner point from the panoramic image may include: and (3) sliding in any direction on the image by using a fixed window, comparing the two conditions before and after sliding, and determining that an angular point exists in the window if the gray degree of the pixel in the window changes greatly if the pixel moves in any direction. In another example, the method of detecting corners may include training a keypoint detection model using a deep learning method, inputting a panoramic image, and outputting individual corners in the panoramic image. In another example, the method for detecting a corner may include a corner detection algorithm based on a gray-scale image, a corner detection algorithm based on a binary image, and a corner detection algorithm based on a contour curve, which is not limited in the embodiments of the present disclosure.
In an embodiment of the present disclosure, a method for obtaining a line segment from the panoramic image may include: the method comprises the steps of firstly calculating the gradient size and the gradient direction of all points in an image, then taking the points with small gradient change and adjacent points as a connected domain, judging whether the points need to be disconnected according to rules according to the rectangularity of each domain to form a plurality of domains with larger rectangularity, and finally improving and screening all the generated domains, and reserving the domains meeting conditions, namely the final straight Line detection result. In another example, the method of obtaining line segments from the panoramic image may further include: hough transform method, FastLineDetector algorithm, and EDlines algorithm. In another example, the image segmentation model may also be trained to identify line segment points in the panoramic image using a deep learning method.
And step S103, acquiring angular points with the interval distance within a preset range from the angular point set, and taking the angular points as angular points of the target parking space.
In the embodiment of the present disclosure, the separation distance may be measured by a pixel distance of the corner point. In an example, a sequence may be performed on the points in the set of points, such as the point a, the point B, and the point C …, where the points in the set of points are determined, for example, a circle is made with the point a as a center and a preset range as a radius, and other points except the point a are searched in the circle. In another example, the distance between every two corner points can be calculated one by one from the corner point set, and the corner points meeting the requirement that the distance is within the preset range are taken as the corner points of the same parking space.
Step S105, obtaining a line segment passing through the corner point from the line segment set.
In the embodiment of the present disclosure, the line segments passing through the corner points may include a line segment passing through one corner point and a line segment passing through two or more corner points. In the embodiment of the present disclosure, the line segment passing through the corner point may be determined by determining a linear expression of the line segment, substituting the coordinates of the corner point into the linear expression, and if the linear expression is satisfied, indicating that the line segment passes through the corner point. The angular point is the angular point of the target parking space, so that a line segment passing through the angular point belongs to the parking space line of the target parking space.
And S107, predicting residual angular points according to the angular points and the line segments, and determining the target parking space.
In the embodiment of the present disclosure, the remaining corner points may be predicted according to the corner points and the line segments. And the residual angular points represent other angular points except the angular points in the target parking space. Specifically, in one example, a line segment passing through one angular point is screened out from the line segments, the angular point is used as an end point, the first line segment is lengthened or shortened to a preset length, and the other end point of the first line segment is used as a remaining angular point of the target parking space. In another example, a line segment passing through two or more corner points is screened out from the line segments, the line segment is used as a bottom edge, the preset length is used as a height, and the shape of the parking space is determined by combining other line segments.
According to the embodiment of the disclosure, under the condition that the information of the target parking space in the panoramic image is not much, namely, the information comprises partial angular points or parking space lines of the parking space, the remaining angular points are predicted through the acquired angular point set and line segment set, so that the target parking space is determined, and the target parking space can be accurately identified.
Fig. 3 is a flow chart illustrating a method of identifying a space in accordance with an exemplary embodiment. Referring to fig. 3, the predicting remaining angular points according to the angular points and the line segments to determine the target parking space includes:
acquiring a first line segment passing through only one corner point from the line segments;
the angular points are used as end points, the first line segment is lengthened or shortened to a preset length, and the other end point of the first line segment is used as a residual angular point of the target parking space;
and determining the target parking space according to the angular points and the residual angular points.
In the embodiment of the present disclosure, referring to fig. 3, the line segments passing through the corner points may include a line segment passing through one corner point, such as line segment 305, line segment 307, and line segment 305, and a line segment passing through two or more corner points, such as line segment 304. The line segments passing through only one of the corner points are taken as the first line segments, i.e., line segment 305, line segment 307, and line segment 305 in the figure. Taking the line segment 304 as an example, an angular point where the line segment 304 passes is an angular point 301, the angular point 301 is an end point, the line segment 305 is extended to a preset degree, the preset length may include a side length of one side of a parallelogram of a target parking space, and another end point of the first line segment is used as a remaining angular point 306 of the target parking space. In the same way as described above, remaining corner point 309 may be determined. And connecting the angular points according to the angular point 301, the angular point 302, the residual angular point 306 and the residual angular point 309 to determine the target parking space.
In the embodiment of the disclosure, the detected corner set and the detected line segment set are used, wherein the position information of the corners in the corner set is used to predict the remaining corners, shapes of the corners are not distinguished any more, and the shapes of the corners can include various shapes, so that parking spaces with various shapes, such as rectangles, parallelograms and the like, can be rapidly identified compared with the prior art.
In one possible implementation, the preset length is set to be obtained as follows:
and converting the parking space side length into a coordinate length in the image according to the proportional relation between the parking space side length and the image coordinate.
In the embodiment of the present disclosure, the length of the parking space side includes a length of an actual side of a parking space, for example, a length of an actual side of a general parking space is 4 to 7 meters. The preset length, such as the distance between the corner point 306 and the corner point 301 in fig. 3, can be preset in the following manner. According to the proportional relation between the parking space side length and the image coordinate, for example, 1 pixel in the image corresponds to an actual distance of 1 cm. And converting the side length of the parking space into the coordinate length in the image according to the proportional relation. And taking the coordinate length as a preset length.
In a possible implementation manner, the obtaining, from the line segment set, a line segment passing through the corner point includes:
establishing an image coordinate system, and determining a linear expression of the line segments in the line segment set;
substituting the angular point into the linear expression, and if the angular point meets the linear expression of the line segment, enabling the line segment to pass through the angular point.
In an embodiment of the present disclosure, an image coordinate system is established, where the image coordinate system may use a preset position of the panoramic image as a coordinate origin, such as an upper left corner position or a center position, an x-axis direction is rightward, and a y-axis direction is downward. And establishing the image coordinate system by taking the horizontal axis of the panoramic image as the horizontal coordinate and the vertical axis of the panoramic image as the vertical coordinate. In the disclosed embodiment, the linear expression of the line segmentFor example, y ═ kx + b, where k denotes the slope and b denotes a constant. The corner point coordinates may be expressed as (x)1，y1) If y is1＝kx1And if the equation of + b is established, the angular point meets the linear expression of the line segment of the book, and the line segment passes through the angular point. In one example, the straight line expression of the line segment may be obtained by fitting with a relevant point set in the panoramic image.
By the method, the relevant line segments and the angular points of the same target parking space can be determined quickly and accurately, and therefore the accuracy of parking space identification is improved.
In a possible implementation manner, the acquiring a set of corner points in the panoramic image includes:
acquiring a panoramic image within a preset range of a vehicle;
inputting the panoramic image into a key point detection model, and outputting the corner position of the parking space in the panoramic image through the key point detection model, wherein the key point detection model is set to be obtained by training by utilizing the corresponding relation between the sample panoramic image and the sample key points.
In the embodiment of the present disclosure, the key point detection model may be based on an artificial intelligence model of machine learning, such as a deep learning model, a reinforcement learning model, and the like. The method for training the key point detection model can comprise the following steps: and acquiring a sample panoramic image, wherein the sample panoramic image is marked with a corner point type. And constructing a key point detection model, wherein training parameters are set in the key point detection model, inputting the sample panoramic image into the key point detection model, and generating a prediction result, namely identifying various types of corner points (key points). And iteratively adjusting the training parameters based on the difference between the prediction result and the marked corner type until the difference meets the preset requirement to obtain a key point detection model.
In a possible implementation manner, the line segment set of the parking space in the panoramic image is obtained. The method comprises the following steps:
acquiring a panoramic image within a preset range of a vehicle;
and inputting the panoramic image into an image segmentation model, and outputting the line segment of the parking space in the panoramic image through the image segmentation model, wherein the image segmentation model is set to be obtained by utilizing the corresponding relation between the sample panoramic image and the sample line segment for training.
In the embodiment of the disclosure, the image segmentation model may be based on an artificial intelligence model of machine learning, such as an FCN network model, a deep lab network model, a Mask R-CNN network model, a U-Net network model, and the like. The training method of the image segmentation model can comprise the following steps: and acquiring a sample panoramic image, wherein line segments are marked in the sample panoramic image. And constructing an image segmentation model, wherein training parameters are set in the image segmentation model, inputting the sample panoramic image into the image segmentation model, and generating a prediction result, namely predicting the category of each pixel in the sample panoramic image, wherein the category can comprise line segments and non-line segments. And iteratively adjusting the training parameters based on the difference between the prediction result and the marked corner type until the difference meets the preset requirement to obtain the image segmentation model.
Fig. 4 is a block diagram of an apparatus for identifying a parking space according to an exemplary embodiment. Referring to fig. 4, the apparatus includes a first obtaining module 401, a second obtaining module 402, a third obtaining module 403, and a determining module 404.
A first obtaining module 401, configured to obtain an angle point set and a line segment set of a parking space in a panoramic image;
a second obtaining module 402, configured to obtain, from the angular point set, angular points with interval distances within a preset range, and use the angular points as angular points of a target parking space;
a third obtaining module 403, configured to obtain a line segment passing through the corner from the line segment set;
and a determining module 404, configured to determine the target parking space according to the angular point and the line segment.
In one possible implementation, the determining module 404 includes:
the obtaining submodule is used for obtaining a first line segment which only passes through one corner point from the line segments;
the adjusting submodule is used for prolonging or shortening the first line segment to a preset length by taking the angular point as an end point, and taking the other end point of the first line segment as a residual angular point of the target parking space;
and the determining submodule is used for determining the target parking space according to the angular point and the residual angular point.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 5 is a block diagram illustrating an apparatus 500 for identifying a space according to an exemplary embodiment. For example, the apparatus 500 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 5, the apparatus 500 may include one or more of the following components: processing component 502, memory 504, power component 506, multimedia component 508, audio component 510, input/output (I/O) interface 512, sensor component 514, and communication component 516.
The processing component 502 generally controls overall operation of the device 500, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 502 may include one or more processors 520 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 502 can include one or more modules that facilitate interaction between the processing component 502 and other components. For example, the processing component 502 can include a multimedia module to facilitate interaction between the multimedia component 508 and the processing component 502.
The memory 504 is configured to store various types of data to support operations at the apparatus 500. Examples of such data include instructions for any application or method operating on device 500, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 504 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 506 provides power to the various components of the device 500. The power components 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 500.
The multimedia component 508 includes a screen that provides an output interface between the device 500 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 508 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 500 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 510 is configured to output and/or input audio signals. For example, audio component 510 includes a Microphone (MIC) configured to receive external audio signals when apparatus 500 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 504 or transmitted via the communication component 516. In some embodiments, audio component 510 further includes a speaker for outputting audio signals.
The I/O interface 512 provides an interface between the processing component 502 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 514 includes one or more sensors for providing various aspects of status assessment for the device 500. For example, the sensor assembly 514 may detect an open/closed state of the apparatus 500, the relative positioning of the components, such as a display and keypad of the apparatus 500, the sensor assembly 514 may also detect a change in the position of the apparatus 500 or a component of the apparatus 500, the presence or absence of user contact with the apparatus 500, orientation or acceleration/deceleration of the apparatus 500, and a change in the temperature of the apparatus 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 516 is configured to facilitate communication between the apparatus 500 and other devices in a wired or wireless manner. The apparatus 500 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 516 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 504 comprising instructions, executable by the processor 520 of the apparatus 500 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Fig. 6 is a block diagram illustrating an apparatus 600 for identifying a space according to an exemplary embodiment. For example, the apparatus 600 may be provided as a server. Referring to fig. 6, the apparatus 600 includes a processing component 622 that further includes one or more processors and memory resources, represented by memory 632, for storing instructions, such as applications, that are executable by the processing component 622. The application programs stored in memory 632 may include one or more modules that each correspond to a set of instructions. Further, the processing component 622 is configured to execute instructions to perform the above-described methods.
The apparatus 600 may also include a power component 626 configured to perform power management of the apparatus 600, a wired or wireless network interface 650 configured to connect the apparatus 600 to a network, and an input output (I/O) interface 655. The apparatus 600 may operate based on an operating system stored in the memory 632, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 632 comprising instructions, executable by the processing component 622 of the apparatus 600 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for identifying a parking space, comprising:
acquiring an angular point set and a line segment set of a parking space in a panoramic image;
acquiring angular points of which the interval distances are within a preset range from the angular point set, and taking the angular points as angular points of the target parking space;
acquiring line segments passing through the corner points from the line segment set;
and predicting residual angular points according to the angular points and the line segments, and determining the target parking space.
2. The method of claim 1, wherein predicting remaining angular points from the angular points and the line segments to determine the target parking space comprises:
acquiring a first line segment passing through only one corner point from the line segments;
the angular points are used as end points, the first line segment is lengthened or shortened to a preset length, and the other end point of the first line segment is used as a residual angular point of the target parking space;
and determining the target parking space according to the angular points and the residual angular points.
3. The method according to claim 2, characterized in that the preset length is set to be obtained in the following way:
and converting the parking space side length into a coordinate length in the image according to the proportional relation between the parking space side length and the image coordinate.
4. The method of claim 1, wherein said obtaining the line segment from the set of line segments that passes through the corner point comprises:
establishing an image coordinate system, and determining a linear expression of the line segments in the line segment set;
substituting the angular point into the linear expression, and if the angular point meets the linear expression of the line segment, enabling the line segment to pass through the angular point.
5. The method of claim 1, wherein the obtaining the set of corners in the panoramic image comprises:
acquiring a panoramic image within a preset range of a vehicle;
inputting the panoramic image into a key point detection model, and outputting the corner position of the parking space in the panoramic image through the key point detection model, wherein the key point detection model is set to be obtained by training by utilizing the corresponding relation between the sample panoramic image and the sample key points.
6. The method of claim 1, wherein the obtaining the set of line segments of the parking space in the panoramic image comprises:
acquiring a panoramic image within a preset range of a vehicle;
and inputting the panoramic image into an image segmentation model, and outputting the line segment of the parking space in the panoramic image through the image segmentation model, wherein the image segmentation model is set to be obtained by utilizing the corresponding relation between the sample panoramic image and the sample line segment for training.
7. The utility model provides a device of discernment parking stall which characterized in that includes:
the first acquisition module is used for acquiring an angular point set and a line segment set of a parking space in the panoramic image;
the second acquisition module is used for acquiring angular points of which the interval distances are within a preset range from the angular point set, and taking the angular points as angular points of a target parking space;
a third obtaining module, configured to obtain a line segment passing through the corner point from the line segment set;
and the determining module is used for predicting the residual angular points according to the angular points and the line segments and determining the target parking space.
8. The apparatus of claim 7, wherein the determining module comprises:
the obtaining submodule is used for obtaining a first line segment which only passes through one corner point from the line segments;
the adjusting submodule is used for prolonging or shortening the first line segment to a preset length by taking the angular point as an end point, and taking the other end point of the first line segment as a residual angular point of the target parking space;
and the determining submodule is used for determining the target parking space according to the angular point and the residual angular point.
9. The utility model provides a device of discernment parking stall which characterized in that includes:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any one of claims 1 to 6.
10. A non-transitory computer-readable storage medium, characterized in that: the instructions in the storage medium, when executed by a processor, enable the processor to perform the method of any of claims 1 to 6.
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