CN115206130B - Parking space detection method, system, terminal and storage medium - Google Patents
Parking space detection method, system, terminal and storage medium Download PDFInfo
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- CN115206130B CN115206130B CN202210815032.1A CN202210815032A CN115206130B CN 115206130 B CN115206130 B CN 115206130B CN 202210815032 A CN202210815032 A CN 202210815032A CN 115206130 B CN115206130 B CN 115206130B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/168—Driving aids for parking, e.g. acoustic or visual feedback on parking space
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The application relates to a parking space detection method, a system, a terminal and a storage medium, wherein the parking space detection method comprises the following steps: acquiring parking space corner information, wherein the parking space corner information comprises current detection corner information and historical detection corner information; outputting target corner information according to the matching condition of the current detection corner information and the historical detection corner information; performing corner classification according to the corner direction in the target corner information; and carrying out corner matching according to the corner classification result and the corner position in the target corner information, and outputting parking space information. According to the parking space detection method, system, terminal and storage medium, the false detection corner information can be filtered out by tracking and screening the parking space corner information identified by the deep learning model, the missing detection corner information is predicted, the corner matching is performed according to the corner classification and the corner position, and the parking space detection rate and detection precision are improved.
Description
Technical Field
The application belongs to the technical field of parking space detection, and particularly relates to a parking space detection method, a parking space detection system, a parking space detection terminal and a storage medium.
Background
With the rapid development of automatic driving technology, various automatic parking auxiliary systems have become standard technology of flagship models of various host manufacturers, and vision-based methods have evolved from the initial ultrasonic-based parking technology.
The vision-based parking auxiliary system is characterized in that a deep learning method is utilized to identify the panoramic image formed by splicing fish-eye camera pictures, and then a series of post-processing is carried out. The traditional post-processing method is to match the identified parking space entrance angular points, mark the rest angular points of the parking space, and then send the parking space angular points to a planning control module to realize automatic parking.
The post-processing method excessively depends on the recognition effect of the deep learning model on the parking space entrance angular points and the calibration value of the parking space size, so that on one hand, the situation of false detection and missing detection of the parking space angular points cannot be avoided; on the other hand, aiming at irregular parking spaces, the calibrated parking space size is likely to be in error, and the parking space detection rate and the detection precision are low.
Disclosure of Invention
Aiming at the technical problems, the application provides a parking space detection method, a parking space detection system, a parking space detection terminal and a storage medium, so as to improve the parking space detection rate and the detection precision.
The application provides a parking space detection method, which comprises the following steps: acquiring parking space corner information, wherein the parking space corner information comprises current detection corner information and historical detection corner information; outputting target corner information according to the matching condition of the current detection corner information and the historical detection corner information; performing corner classification according to the corner direction in the target corner information; and carrying out corner matching and outputting parking space information according to corner classification results and corner positions in the target corner information.
In an embodiment, the step of outputting target corner information according to the matching condition of the current detected corner information and the historical detected corner information includes: acquiring first corner information successfully matched in the current detection corner information and the historical detection corner information; acquiring second corner information which is in the history detection corner information and fails to match with the current detection corner information, wherein the number of times of successful history matching is greater than a preset number of times; and outputting the first corner information and the second corner information.
In an embodiment, before the step of obtaining the first corner information of successful matching in the current corner information and the historical corner information, the method includes: predicting the historical detection corner information to obtain prediction corner information; updating the historical detection corner information into the prediction corner information; before outputting the first angle point information, the method includes: and carrying out smooth optimization on the first corner information.
In an embodiment, the step of outputting target corner information according to the matching condition of the current detected corner information and the historical detected corner information includes: and acquiring third corner information which fails to be matched with the historical detection corner information in the current detection corner information, and storing the third corner information into the historical detection corner information.
In an embodiment, the step of performing corner classification according to the corner direction in the target corner information includes: if the angle difference value between the directions of any two corner points in the target corner point information meets a first threshold range, the corresponding two corner points are homodromous corner points; and if the angle difference value between the directions of any two corner points in the target corner point information meets a second threshold range, the corresponding two corner points are reverse corner points.
In an embodiment, the step of performing corner matching and outputting parking space information according to the corner classification result and the corner position in the target corner information includes: according to the corner classification result and the corner position in the target corner information, respectively obtaining the distance between adjacent corners in the same-direction corner and the distance between adjacent corners in the opposite-direction corner; and matching the adjacent corner points of which the distances in the same-direction corner points meet a third threshold range and the adjacent corner points of which the distances in the reverse corner points meet a fourth threshold range, and outputting the corner point positions and the parking space sizes of the same parking space.
In an embodiment, the step of performing corner matching and outputting parking space information according to the corner classification result and the corner position in the target corner information further includes: according to the corner classification result and the corner position in the target corner information, obtaining the distance between adjacent corners in the same-direction corner or the opposite-direction corner; outputting the corner positions of the same parking space according to the information of the adjacent corner points, the distance of which in the same-direction corner points meets a third threshold range, and the preset parking space size; or outputting the corner positions of the same parking space according to the information of the adjacent corner points, the distance of which in the reverse corner points meets the fourth threshold range, and the preset parking space size.
The application also provides a parking space detection system, which comprises an acquisition module, an angular point tracking module, an angular point classification module and an angular point matching module; the parking space corner information comprises current detection corner information and historical detection corner information; the corner tracking module is used for outputting target corner information according to the matching condition of the current detected corner information and the historical detected corner information; the corner classification module is used for performing corner classification according to the corner direction in the target corner information; and the corner matching module is used for performing corner matching and outputting parking space information according to the corner classification result and the corner position in the target corner information.
The application also provides a terminal, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the parking space detection method.
The application also provides a storage medium storing a computer program which, when executed by a processor, implements the steps of the parking space detection method described above.
According to the parking space detection method, system, terminal and storage medium, the false detection corner information can be filtered out by tracking and screening the parking space corner information identified by the deep learning model, the missing detection corner information is predicted, the corner matching is performed according to the corner classification and the corner position, and the parking space detection rate and detection precision are improved.
Drawings
Fig. 1 is a schematic view of a parking panorama image according to an embodiment of the present application;
fig. 2 is a flow chart of a parking space detection method according to an embodiment of the present application;
fig. 3 is a schematic view of a corner classification effect provided in the first embodiment of the present application;
fig. 4 is a schematic diagram of a corner matching effect provided in the first embodiment of the present application;
fig. 5 is a schematic structural diagram of a parking space detection system according to a second embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal according to a third embodiment of the present application.
Detailed Description
The technical scheme of the application is further elaborated below by referring to the drawings in the specification and the specific embodiments. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a schematic view of a panoramic image according to an embodiment of the present application. The panoramic image is obtained by splicing pictures acquired by four paths of fisheye cameras arranged on a vehicle, wherein the four paths of fisheye cameras are respectively arranged right in front of, right behind, left in front of and right in front of the vehicle, and the cameras in front of left and right in front of are respectively arranged on a left rearview mirror and a right rearview mirror of the vehicle; optionally, an image coordinate system is established by taking the upper left corner of the panoramic image as the origin of coordinates, and the parking space detection method provided by the embodiment of the application is realized based on the image coordinate system of the panoramic image; in fig. 1, O represents the origin of coordinates, X represents the X-axis of the image coordinate system, Y represents the Y-axis of the image coordinate system, P represents the parking space, and C represents the vehicle to be parked.
Fig. 2 is a flow chart of a parking space detection method according to an embodiment of the present application. As shown in fig. 2, the parking space detection method of the present application may include the following steps:
step S101: acquiring parking space corner information, wherein the parking space corner information comprises current detection corner information and historical detection corner information;
optionally, performing image segmentation processing on the panoramic image through a deep learning model to detect vehicle position angle point information, wherein the current detected angle point information is the vehicle position angle point information obtained by real-time detection; the historical detection corner information is parking space corner information obtained through historical detection; the parking space corner information comprises coordinates and directions of the parking space corner under an image coordinate system.
Step S102: outputting target corner information according to the matching condition of the current detection corner information and the historical detection corner information;
in one embodiment, step S102 includes:
acquiring first corner information successfully matched in current detection corner information and historical detection corner information;
acquiring second corner information which is in the history detection corner information and fails to match with the current detection corner information, wherein the number of times of successful history matching is greater than the preset number of times;
and outputting the first corner information and the second corner information.
In an embodiment, before the step of obtaining the first corner information of successful matching in the current corner information and the historical corner information, the method includes:
predicting the historical detection corner information to obtain prediction corner information;
updating the historical detection corner information into prediction corner information;
before outputting the first corner information, comprising: and carrying out smooth optimization on the first angle point information.
In one embodiment, step S102 includes:
and acquiring third corner information which fails to be matched with the historical detection corner information in the current detection corner information, and storing the third corner information into the historical detection corner information.
Optionally, prediction of the historical detection corner information and smooth optimization of the first corner information are achieved through a Kalman filter. Smoothing filtering is carried out on the corner information successfully matched with the current detected corner information and the historical corner information, and then the corner information is output; further judging the number of times of history matching success aiming at the history corner information which is not successfully matched with the current detection corner information, and outputting when the number of times of history matching success is greater than the preset number of times, such as 5 times; and aiming at the current detection corner information which is not successfully matched with the historical corner information, storing the current detection corner information into the historical detection corner information, and temporarily outputting the current detection corner information.
Illustratively, the current detected corner information of the first frame image comprises A1 corner information and B1 corner information, and the current detected corner information of the first frame image is stored in the historical detected corner information and is temporarily not output as the first frame image is matched without the historical detected corner information; the current detected corner information of the second frame of image comprises A2 corner information, B2 corner information and C2 corner information; predicting historical detection corner information stored in a first frame image, if the A1 corner information and the B1 corner information in the first frame image are the A2 corner information and the B2 corner information respectively, the A2 corner information and the B2 corner information in the second frame image are successfully matched with the A1 corner information and the B1 corner information in the historical corner information, the A2 corner information and the B2 corner information are output, the A1 corner information and the B1 corner information in the historical corner information are respectively updated to the A2 corner information and the B2 corner information, C2 corner information which is not successfully matched with the historical detection corner information in the second frame image is stored to the historical detection corner information, and the finally stored historical detection corner information of the second frame image comprises the A2 corner information, the B2 corner information and the C2 corner information; historical detection corner information stored for the second frame image: a2 corner information, B2 corner information, C2 corner information, predicted corner information in the third frame image, such as A3 corner information, B3 corner information, C3 corner information, the history matching success times are 1, 1 and 0 respectively.
Step S103: performing corner classification according to the corner direction in the target corner information;
in one embodiment, step S103 includes:
if the angle difference value between the directions of any two corner points in the target corner point information meets a first threshold range, the corresponding two corner points are homodromous corner points;
if the angle difference value between the directions of any two corner points in the target corner point information meets a second threshold range, the corresponding two corner points are reverse corner points.
Optionally, a clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCN) is adopted, and the corner points in the target corner point information are classified through a DBSCAN classifier to obtain similar corner point information and heterogeneous corner point information, wherein the similar corner point information is the homodromous corner point information and can be directly output; for the heterogeneous angular point information, further judging whether the angle difference value between the directions of any two angular points meets a second threshold range or not, and outputting reverse angular point information of which the angle difference value meets the second threshold range; optionally, the first threshold range is [0,30], and the second threshold range is [150,200], in degrees.
As shown in fig. 3, A1, A2, A3, A4 are corner points, A1', A2', A3', A4' are corner points, any point of A1, A2, A3, A4 and any point of A1', A2', A3', A4' are corner points in opposite directions, and an arrow in fig. 3 indicates a direction carried by the corner points.
Step S104: and carrying out corner matching according to the corner classification result and the corner position in the target corner information, and outputting parking space information.
Optionally, the parking space information includes positions of four corner points of the parking space in an image coordinate system and length and width dimensions of the parking space.
In one embodiment, step S104 includes:
according to the corner classification result and the corner position in the target corner information, respectively obtaining the distance between adjacent corners in the same-direction corner and the distance between adjacent corners in the opposite-direction corner;
and matching the adjacent corner points of which the distances in the same-direction corner points meet the third threshold range and the adjacent corner points of which the distances in the reverse corner points meet the fourth threshold range, and outputting the corner point positions and the parking space sizes of the same parking space.
Optionally, according to the corner classification result and the corner position in the target corner information, calculating the distance between any two adjacent corners in the same-direction corner, pairing the adjacent corners with the distance in the same-direction corner meeting a third threshold range, calculating the distance between any two adjacent corners in the reverse corner, and pairing the adjacent corners with the distance in the reverse corner meeting a fourth threshold range; and then matching the co-directional corner points successfully matched with the reverse corner points successfully matched, outputting corner point information capable of forming the same parking space, and calculating the size of the parking space according to the outputted corner point information.
As shown in fig. 4, the co-directional corner points of successful pairing include B1 and B2, B2 and B3, B3 and B4, B1 'and B2', B2 'and B3', and B3 'and B4', the reverse-directional corner points of successful pairing include B1 and B1', B2 and B2', B3 and B3', and B4', and the matching is performed on the co-directional corner points of successful pairing and the reverse-directional corner points of successful pairing to obtain corner point combinations including B1, B2, B1 'and B2' capable of forming the same parking space; b2, B3, B2 'and B3'; b3, B4, B3 'and B4'; in fig. 4, an arrow indicates a direction carried by a corner point, and P indicates a detected parking space.
It is worth mentioning that the parking stall detection method that this application provided does not strongly depend on the calibration value of parking stall size, when the corner is mated, can be on the basis of corner classification purposefully carry out the corner distance judgement, and the corner distance satisfies the threshold value scope, can confirm to mate successfully, then matches the syntropy corner that pairs successfully with reverse corner, and the output can constitute the corner information of same parking stall, can avoid because the problem that the parking stall detection precision is insufficient that the error of parking stall size calibration value brought.
In an embodiment, step S104 further includes:
according to the corner classification result and the corner position in the target corner information, the distance between adjacent corners in the same-direction corner or opposite-direction corner is obtained;
outputting the corner positions of the same parking space according to the information of the adjacent corner points, the distance of which in the same corner point meets a third threshold range, and the preset parking space size; or (b)
And outputting the corner positions of the same parking space according to the information of the adjacent corner points, the distance of which in the reverse corner points meets the fourth threshold range, and the preset parking space size.
Optionally, under the condition that four corner points of the same parking space cannot be acquired at the same time, if any two adjacent corner points in the same-direction or reverse corner points are successfully paired, the positions of the other two corner points can be deduced by combining the preset parking space size, and the problem of missed detection caused by shielding of part of corner points in the traditional parking space detection method can be avoided.
Optionally, the third threshold range is [2.10,2.92], the fourth threshold range is [5.85,6.56], in meters.
According to the parking space detection method provided by the embodiment of the application, the current detection corner information is tracked and screened through the historical detection corner information, the false detection corner information is filtered, the missing detection corner information is predicted and output in a complementary mode, the corner matching is carried out according to the corner classification and the corner position, and the parking space detection rate and the detection precision are improved.
Fig. 5 is a schematic structural diagram of a parking space detection system according to a second embodiment of the present application. As shown in fig. 5, the parking space detection system of the present application includes an acquisition module 11, an angular point tracking module 12, an angular point classification module 13, and an angular point matching module 14;
the acquiring module 11 is configured to acquire parking space corner information, where the parking space corner information includes current detection corner information and historical detection corner information;
the corner tracking module 12 is used for outputting target corner information according to the matching condition of the current detected corner information and the historical detected corner information;
the corner classification module 13 is used for performing corner classification according to the corner direction in the target corner information;
the corner matching module 14 is configured to perform corner matching and output parking space information according to the corner classification result and the corner position in the target corner information.
The specific implementation method of this embodiment is referred to in the first embodiment, and will not be described herein.
According to the parking space detection system provided by the second embodiment of the application, through interaction among the acquisition module, the corner tracking module, the corner classifying module and the corner matching module, the parking space corner information is tracked and screened, false detection corner information is filtered, missed detection corner information is predicted, corner matching is performed according to corner classification and corner positions, and the parking space detection rate and detection accuracy are improved.
Fig. 6 is a schematic structural diagram of a terminal according to an embodiment three of the present application. The terminal of the application comprises: a processor 110, a memory 111 and a computer program 112 stored in said memory 111 and executable on said processor 110. The steps of the above embodiments of the method for detecting a parking space are implemented by the processor 110 when the computer program 112 is executed.
The terminal may include, but is not limited to, a processor 110, a memory 111. It will be appreciated by those skilled in the art that fig. 6 is merely an example of a terminal and is not intended to be limiting, and that more or fewer components than shown may be included, or certain components may be combined, or different components may be included, for example, the terminal may also include input and output devices, network access devices, buses, etc.
The processor 110 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 111 may be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 111 may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the terminal. Further, the memory 111 may also include both an internal storage unit and an external storage device of the terminal. The memory 111 is used for storing the computer program and other programs and data required by the terminal. The memory 111 may also be used to temporarily store data that has been output or is to be output.
The present application also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the parking spot detection method as described above.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a list of elements is included, and may include other elements not expressly listed.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The parking space detection method is characterized by comprising the following steps of:
acquiring parking space corner information, wherein the parking space corner information comprises current detection corner information and historical detection corner information;
outputting target corner information according to the matching condition of the current detection corner information and the historical detection corner information;
according to the angular point direction in the target angular point information, carrying out angular point classification, and determining the homodromous angular point and/or the reverse angular point;
according to the corner classification result and the corner position in the target corner information, the distance between adjacent corners in the homodromous corner and/or the distance between adjacent corners in the reverse corner are obtained;
and carrying out corner matching and outputting parking space information according to the distance between adjacent corner points in the same-direction corner points and/or the distance between adjacent corner points in the reverse corner points.
2. The parking space detection method according to claim 1, wherein the step of outputting target corner information according to the matching condition of the current detected corner information and the history detected corner information comprises:
acquiring first corner information successfully matched in the current detection corner information and the historical detection corner information;
acquiring second corner information which is in the history detection corner information and fails to match with the current detection corner information, wherein the number of times of successful history matching is greater than a preset number of times;
and outputting the first corner information and the second corner information.
3. The parking space detection method according to claim 2, comprising, before the step of acquiring the first corner information of successful matching of the current detected corner information and the history detected corner information:
predicting the historical detection corner information to obtain prediction corner information;
updating the historical detection corner information into the prediction corner information;
before outputting the first angle point information, the method includes: and carrying out smooth optimization on the first corner information.
4. The parking space detection method according to claim 2, wherein the step of outputting target corner information according to the matching condition of the current detected corner information and the history detected corner information comprises:
and acquiring third corner information which fails to be matched with the historical detection corner information in the current detection corner information, and storing the third corner information into the historical detection corner information.
5. The parking space detection method according to claim 1, wherein the step of performing corner classification according to the corner direction in the target corner information to determine the co-directional corner and/or the counter-directional corner comprises:
if the angle difference value between the directions of any two corner points in the target corner point information meets a first threshold range, the corresponding two corner points are homodromous corner points;
and if the angle difference value between the directions of any two corner points in the target corner point information meets a second threshold range, the corresponding two corner points are reverse corner points.
6. The parking space detection method according to claim 1, wherein the step of performing corner matching and outputting parking space information according to a distance between adjacent ones of the co-directional corner points and/or a distance between adjacent ones of the reverse corner points comprises:
and matching the adjacent corner points of which the distances in the same-direction corner points meet a third threshold range and the adjacent corner points of which the distances in the reverse corner points meet a fourth threshold range, and outputting the corner point positions and the parking space sizes of the same parking space.
7. The parking space detection method according to claim 1, wherein the step of performing corner matching and outputting parking space information according to a distance between adjacent ones of the co-directional corner points and/or a distance between adjacent ones of the reverse corner points comprises:
outputting the corner positions of the same parking space according to the information of the adjacent corner points, the distance of which in the same-direction corner points meets a third threshold range, and the preset parking space size; or (b)
And outputting the corner positions of the same parking space according to the information of the adjacent corner points, the distance of which in the reverse corner points meets the fourth threshold range, and the size of the preset parking space.
8. The parking space detection system is characterized by comprising an acquisition module, an angular point tracking module, an angular point classification module and an angular point matching module;
the parking space corner information comprises current detection corner information and historical detection corner information;
the corner tracking module is used for outputting target corner information according to the matching condition of the current detected corner information and the historical detected corner information;
the corner classification module is used for carrying out corner classification according to the corner direction in the target corner information and determining the same-direction corner and/or reverse corner;
the corner matching module is used for acquiring the distance between adjacent corners in the same-direction corner and/or the distance between adjacent corners in the reverse corner according to the corner classification result and the corner position in the target corner information, performing corner matching according to the distance between adjacent corners in the same-direction corner and/or the distance between adjacent corners in the reverse corner, and outputting parking space information.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the parking spot detection method according to any one of claims 1 to 7 when the computer program is executed.
10. A storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the parking space detection method according to any one of claims 1 to 7.
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