CN111507269A - Parking space state identification method and device, storage medium and electronic device - Google Patents
Parking space state identification method and device, storage medium and electronic device Download PDFInfo
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- CN111507269A CN111507269A CN202010307126.9A CN202010307126A CN111507269A CN 111507269 A CN111507269 A CN 111507269A CN 202010307126 A CN202010307126 A CN 202010307126A CN 111507269 A CN111507269 A CN 111507269A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/20—Scenes; Scene-specific elements in augmented reality scenes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
- G06V10/464—Salient features, e.g. scale invariant feature transforms [SIFT] using a plurality of salient features, e.g. bag-of-words [BoW] representations
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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
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
Abstract
The invention provides a parking space state identification method and device, a storage medium and an electronic device; wherein, the method comprises the following steps: acquiring a plurality of images shot for a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod; recognizing the occupation state of the parking space in a preset area in the plurality of images and the number of the marks arranged on the crash bar; and determining the parking space state of the preset area based on the parking space occupation state indicated by the identification result and the number of the identifications. The method and the device solve the problem of low accuracy in identifying the state of the logistics park platform in a mode of setting the mark point and directly identifying the parking space in the related technology.
Description
Technical Field
The invention relates to the field of computers, in particular to a parking space state identification method and device, a storage medium and an electronic device.
Background
At present, the dispatching of trucks in a logistics park mainly depends on a manual management method, in order to reduce the management difficulty and simplify the management flow, some logistics parks adopt a mode of limiting the number of trucks entering the park, namely only the trucks to be loaded and unloaded are allowed to enter the logistics park, and on one hand, the method causes that the original design capacity of the logistics park cannot be effectively exerted; on the other hand, the undischarged trucks are retained on urban traffic roads outside the logistics park, so that traffic jam in the area is caused, and the trips of other local enterprises and residents are influenced. Therefore, the real-time monitoring of the operation platform state inside the logistics park is critical, and then the platform state is identified, if the operation is performed, whether the parking space is occupied or not is judged.
The existing parking space occupation condition identification technology utilizes a sensor network to detect and manage whether a parking space is occupied or not, and the method has large workload of equipment installation and maintenance and higher cost; the method includes (1) setting a mark point in a parking space area, judging whether a vehicle exists in the current position according to whether the mark point is shielded by the vehicle, and (2) directly identifying whether the vehicle exists in the parking space to judge whether the vehicle exists in the current position under the influence of the environment by the set mark point, wherein the method is poor in robustness.
In view of the above problems in the related art, no effective solution exists at present.
Disclosure of Invention
The embodiment of the invention provides a parking space state identification method and device, a storage medium and an electronic device, and aims to at least solve the problem of low accuracy in identification of the state of a logistics park platform in a manner of setting a mark point and directly identifying a parking space in the related art.
According to an embodiment of the present invention, a method for identifying a parking space state is provided, including: acquiring a plurality of images shot for a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod; recognizing the occupation state of the parking space in a preset area in the plurality of images and the number of the marks arranged on the crash bar; and determining the parking space state of the preset area based on the parking space occupation state indicated by the identification result and the number of the identifications.
According to another embodiment of the present invention, there is provided a parking space state recognition apparatus including: the device comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a plurality of images for shooting a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod; the identification module is used for identifying the occupied state of the parking space in the preset area in the plurality of images and the number of the marks arranged on the crash bar; and the determining module is used for determining the parking space state of the preset area based on the parking space occupation state indicated by the identification result and the number of the identifications.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, the parking space state identification accuracy is improved by identifying the parking space occupation state in the preset area and the number of the marks on the anti-collision rod, and the problem of low identification accuracy of the state of the logistics park platform by setting the mark point and directly identifying the parking space is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a parking space state identification method according to an embodiment of the present invention;
FIG. 2 is a first schematic diagram of a sample image according to an embodiment of the invention;
FIG. 3 is a second schematic diagram of a sample image according to an embodiment of the invention;
FIG. 4 is a third schematic diagram of a sample image according to an embodiment of the invention;
FIG. 5 is a fourth schematic diagram of a sample image according to an embodiment of the invention;
FIG. 6 is a fifth schematic diagram of a sample image according to an embodiment of the invention;
FIG. 7 is a sixth schematic representation of a sample image according to an embodiment of the invention;
fig. 8 is a schematic structural diagram of a parking space state recognition device according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example 1
In this embodiment, a method for identifying a parking space state is provided, and fig. 1 is a flowchart of a method for identifying a parking space state according to an embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:
step S102, acquiring a plurality of images for shooting a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod;
step S104, recognizing the occupied state of the parking space in the preset area in the plurality of images and the number of the marks arranged on the crash bar;
and S106, determining the parking space state of the preset area based on the parking space occupation state indicated by the identification result and the number of the identifications.
Through the steps S102 to S106, the parking space state identification accuracy is improved by identifying the parking space occupation state in the preset area and the number of the marks on the crash bar, and the problem of low identification accuracy of the state of the logistics park platform by setting the mark point and directly identifying the parking space is solved.
Optionally, in this embodiment, the manner of identifying the occupancy states of the parking spaces in the preset areas in the multiple images and the number of the identifiers arranged on the crash bar, which is referred to in step S104, may further be: recognizing the occupation state of the parking space in a preset area in the plurality of images and the number of the identifiers arranged on the crash bar through the trained network model; the trained network model is obtained by using a plurality of sample images to train a primary network model; the parking space occupation state of the preset area in the sample image comprises the following steps: a fully occupied state, an unoccupied state, and an incompletely occupied state; the anti-collision rod in the sample image is marked by two different colors, and the two colors marked continuously form a mark.
Optionally, the sample image is an image subjected to matting processing in this embodiment.
For the step S104, taking the preset area as the platform of the logistics park as an example, as shown in fig. 2, the parking space detection area is known in advance by using a manual labeling manner; further, in order to facilitate state identification, the parking space area is subjected to cutout and perspective transformation, as shown in fig. 3. And to the parking stall occupation state in the sample image, this state includes: a fully occupied state, an unoccupied state, and an incompletely occupied state; the three states in the logistic park are shown in fig. 4; from left to right are: unoccupied state, incompletely occupied state, and completely occupied state. In a specific implementation mode, a deep learning method is used, specifically, the modified VGG network is used for training sample image data to obtain a classification network model of a parking space state, and then the network model is used for identifying the state of a map to be tested.
Optionally, in this embodiment, the parking space occupation state identification mode includes at least one of the following:
(1) under the condition that the parking space occupation states of the images exceeding the first preset number in the plurality of images are identified as completely occupied states through the trained network model, obtaining an identification result for indicating that the preset area is occupied;
taking the preset area as the logistics platform as an example, the process shows that the platform is always occupied before the plurality of images are acquired, so that the acquired images are all in an occupied state.
(2) Under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as an unoccupied state through the trained network model, obtaining an identification result for indicating that the preset area is unoccupied;
taking a preset area as a logistics platform as an example, the process refers to a state that the platform is not occupied until the plurality of images are acquired.
(3) Under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as a change process from a completely occupied state to an incompletely occupied state and then to an unoccupied state through the trained network model, obtaining an identification result for indicating that the preset area is unoccupied;
taking the preset area as the logistics platform as an example, the process indicates that the vehicle of the platform leaves the platform.
(4) Under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as a change process from an unoccupied state to an incompletely occupied state and then to a completely occupied state through the trained network model, an identification result used for indicating that the preset area is occupied is obtained.
Taking the preset area as a logistics platform as an example, the process indicates that a vehicle enters the platform.
It should be noted that the first preset number may be taken as a value according to an actual situation, for example, if the number of the images is 50, the first preset number may be 40 or another value. The purpose is to avoid misidentification.
Optionally, in this embodiment, the identification manner for the number of identifiers includes at least one of the following:
(1) under the condition that the number of marks formed by two continuously marked colors in the images exceeds a second preset number through the trained network model, obtaining a recognition result for indicating that the crash bar is not blocked;
(2) and obtaining a recognition result for indicating that the crash bar is blocked under the condition that the number of the marks consisting of two continuously marked colors in the plurality of images does not exceed a second preset number through the trained network model.
It should be noted that the value of the second preset number may be correspondingly taken according to an actual situation.
In the specific implementation of this embodiment, taking a logistics garden platform as an example, a crash bar detection area needs to be known in advance by using a manual labeling mode, as shown in fig. 5, in order to facilitate state identification, the crash bar detection area is subjected to matting processing, as shown in fig. 6, further, whether the crash bar is blocked or not is identified, and a detection target is a "yellow-black" block and/or a "black-yellow" block, a deep learning method is used, and an optimized YO L OV3 network is specifically used to train corresponding image data to obtain a relevant detection network and a detection model, and then the network and the model are used to identify an image to be detected, with the identification effect as shown in fig. 7.
Optionally, in this embodiment, the method for determining the parking space state of the preset area based on the parking space occupation state indicated by the recognition result and the number of the identifiers in step S106 includes the following several methods:
(1) determining that the parking space state is a normal occupation state under the condition that the recognition result indicates that the preset area is occupied and the anti-collision fence is blocked;
(2) determining that the parking space state is an idle state under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision fence is not blocked;
(3) determining that the parking space state is an abnormal state under the condition that the identification result indicates that the preset area is occupied and the anti-collision fence is not blocked;
(4) and determining that the parking space state is an abnormal state under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision fence is blocked.
Not only can discern the normal occupation state of parking stall state and idle state through above-mentioned four kinds of modes, can also discern abnormal state, promptly, this embodiment utilizes the scene to have this article of crash bar, combines the method that detects, discerns whether the crash bar is sheltered from, and the crash bar shelters from the condition and combines the output platform final state that the parking stall occupation condition can be more accurate, two discernment states can be good after combining resist a lot of abnormal conditions, so the accuracy is high and the robustness is good.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
In this embodiment, a device for identifying a parking space state is further provided, and the device is used to implement the above embodiments and preferred embodiments, which have already been described and will not be described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 8 is a block diagram of a parking space status recognition device according to an embodiment of the present invention, and as shown in fig. 8, the device includes:
(1) an obtaining module 82, configured to obtain a plurality of images obtained by shooting a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod;
(2) the identification module 84 is used for identifying the occupied state of the parking space in the preset area in the plurality of images and the number of the marks arranged on the crash bar;
(3) and the determining module 86 is configured to determine the parking space state of the preset area based on the parking space occupation state indicated by the identification result and the number of the identifiers.
Optionally, the recognition module 84 is further configured to recognize, through the trained network model, the occupancy states of the parking spaces in the preset areas in the multiple images and the number of the identifiers arranged on the crash bar; the trained network model is obtained by using a plurality of sample images to train a primary network model; the parking space occupation state of the preset area in the sample image comprises the following steps: a fully occupied state, an unoccupied state, and an incompletely occupied state; the anti-collision rod in the sample image is marked by two different colors, and the two colors marked continuously form a mark.
Optionally, the parking space occupation state identification mode in this embodiment includes at least one of the following:
under the condition that the parking space occupation states of the images exceeding the first preset number in the plurality of images are identified as completely occupied states through the trained network model, obtaining an identification result for indicating that the preset area is occupied;
under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as an unoccupied state through the trained network model, obtaining an identification result for indicating that the preset area is unoccupied;
under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as a change process from a completely occupied state to an incompletely occupied state and then to an unoccupied state through the trained network model, obtaining an identification result for indicating that the preset area is unoccupied;
under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as a change process from an unoccupied state to an incompletely occupied state and then to a completely occupied state through the trained network model, an identification result used for indicating that the preset area is occupied is obtained.
Optionally, the identification manner of the number of identifiers in this embodiment includes at least one of the following:
under the condition that the number of marks formed by two continuously marked colors in the images exceeds a second preset number through the trained network model, obtaining a recognition result for indicating that the crash bar is not blocked;
and obtaining a recognition result for indicating that the crash bar is blocked under the condition that the number of the marks consisting of two continuously marked colors in the plurality of images does not exceed a second preset number through the trained network model.
Optionally, the determining module 86 in this embodiment includes: the first determining unit is used for determining that the parking space state is a normal occupation state under the condition that the recognition result indicates that the preset area is occupied and the anti-collision fence is blocked; the second determining unit is used for determining that the parking space state is an idle state under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision fence is not blocked; the third determining unit is used for determining that the parking space state is an abnormal state under the condition that the recognition result indicates that the preset area is occupied and the anti-collision fence is not blocked; and the fourth determining unit is used for determining that the parking space state is an abnormal state under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision fence is blocked.
It should be noted that the sample image in this embodiment is an image subjected to matting processing.
Optionally, the apparatus of this embodiment may further include: and the sending module is used for sending a reminding message under the condition that the parking space state is identified to be an abnormal state.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 3
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring a plurality of images for shooting a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod;
s2, recognizing the occupied state of the parking space in the preset area in the plurality of images and the number of the marks arranged on the crash bar;
and S3, determining the parking space state of the preset area based on the parking space occupation state indicated by the recognition result and the number of the identifications.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring a plurality of images for shooting a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod;
s2, recognizing the occupied state of the parking space in the preset area in the plurality of images and the number of the marks arranged on the crash bar;
and S3, determining the parking space state of the preset area based on the parking space occupation state indicated by the recognition result and the number of the identifications.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides a parking stall state's identification method which characterized in that includes:
acquiring a plurality of images shot for a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod;
recognizing the occupation state of the parking space in a preset area in the plurality of images and the number of the marks arranged on the crash bar;
and determining the parking space state of the preset area based on the parking space occupation state indicated by the identification result and the number of the identifications.
2. The method of claim 1, wherein the identifying the occupancy status of the parking spaces in the predetermined area in the plurality of images and the number of the crash bar identifiers comprises:
recognizing the occupation state of the parking space in the preset area in the plurality of images and the number of the anti-collision rod marks through the trained network model; the trained network model is obtained by using a plurality of sample images to train a primary network model; the parking space occupation state of the preset area in the sample image comprises the following steps: a fully occupied state, an unoccupied state, and an incompletely occupied state; the bumper bar in the sample image is marked by two different colors, and the two colors marked continuously form the mark.
3. The method according to claim 2, wherein the parking space occupancy status identification manner comprises at least one of:
under the condition that the parking space occupation states of the images exceeding the first preset number in the plurality of images are identified as completely occupied states through the trained network model, obtaining an identification result used for indicating that the preset area is occupied;
under the condition that the parking space occupation states of the images exceeding the first preset number in the plurality of images are identified as unoccupied states through the trained network model, obtaining an identification result for indicating that the preset area is unoccupied;
under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as a change process from a completely occupied state to an incompletely occupied state and then to an unoccupied state through the trained network model, obtaining an identification result for indicating that the preset area is unoccupied;
and under the condition that the parking space occupation state of the images exceeding the first preset number in the plurality of images is identified as a change process from an unoccupied state to an incompletely occupied state and then to a completely occupied state through the trained network model, obtaining an identification result for indicating that the preset area is occupied.
4. The method of claim 3, wherein the identification of the number of identities comprises at least one of:
obtaining a recognition result for indicating that the crash bar is not blocked under the condition that the number of the marks consisting of two continuously marked colors in the plurality of images exceeds a second preset number through the trained network model;
and obtaining a recognition result for indicating that the crash bar is blocked under the condition that the number of the marks consisting of two continuously marked colors in the plurality of images does not exceed a second preset number through the trained network model.
5. The method according to claim 4, wherein determining the parking space state of the preset area based on the parking space occupation state indicated by the recognition result and the number of the identifications comprises:
determining that the parking space state is a normal occupation state under the condition that the identification result indicates that the preset area is occupied and the anti-collision fence is blocked;
determining that the parking space state is an idle state under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision fence is not blocked;
determining that the parking space state is an abnormal state under the condition that the identification result indicates that the preset area is occupied and the anti-collision fence is not blocked;
and determining that the parking space state is an abnormal state under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision fence is blocked.
6. The method of claim 5, further comprising:
and sending a reminding message when the parking space state is identified to be an abnormal state.
7. The method of claim 2, wherein the sample image is a matte-processed image.
8. The utility model provides an identification means of parking stall state which characterized in that includes:
the device comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a plurality of images for shooting a preset area; the system comprises a preset area, a control system and a control system, wherein the preset area is used for parking a vehicle, and an anti-collision rod for collision prevention is arranged in the preset area; a plurality of marks are arranged on the anti-collision rod;
the identification module is used for identifying the occupied state of the parking space in the preset area in the plurality of images and the number of the marks arranged on the crash bar;
and the determining module is used for determining the parking space state of the preset area based on the parking space occupation state indicated by the identification result and the number of the identifications.
9. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 7 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112784794A (en) * | 2021-01-29 | 2021-05-11 | 深圳市捷顺科技实业股份有限公司 | Vehicle parking state detection method and device, electronic equipment and storage medium |
| CN115938153A (en) * | 2022-10-31 | 2023-04-07 | 西安建筑科技大学 | Outdoor parking lot real-time parking space display method, device, equipment and storage medium |
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