CN111507269B - 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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- CN111507269B CN111507269B CN202010307126.9A CN202010307126A CN111507269B CN 111507269 B CN111507269 B CN 111507269B CN 202010307126 A CN202010307126 A CN 202010307126A CN 111507269 B CN111507269 B CN 111507269B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- G06V20/20—Scenes; Scene-specific elements in augmented reality scenes
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- G06—COMPUTING; CALCULATING OR COUNTING
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- 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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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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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 in a preset area; the preset area is used for parking a vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks; identifying the parking space occupation state in a preset area in the images and the number of marks arranged on the anti-collision rod; 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 marks. The method and the device solve the problem of low accuracy in identifying the state of the platform of the logistics park by setting the mark point and directly identifying the parking space in the related art.
Description
Technical Field
The present invention relates to the field of computers, and in particular, to a method and apparatus for identifying a parking space state, a storage medium, and an electronic apparatus.
Background
At present, the dispatching of trucks in a logistics park mainly depends on a manual management method, in order to reduce management difficulty and simplify management flow, some logistics parks adopt a mode of limiting the number of trucks entering the logistics park, namely, only loading and unloading operation trucks 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 stay on urban traffic roads outside the logistics park to cause traffic jam in the area and influence the travel of other local enterprises and residents. Therefore, the real-time monitoring of the status of the operation platform in the logistics park is critical, and then the status of the platform is identified, such as whether the operation is in progress, i.e. whether the parking space is occupied.
The existing parking space occupation condition recognition technology utilizes a sensor network to realize detection and management on whether the parking space is occupied or not, and the method has the advantages of large equipment installation and maintenance workload and high cost; the method mainly comprises the steps of (1) setting a mark point in a parking space area, judging whether the current position has a car or not according to whether the mark point is shielded by the car, and (2) directly identifying whether the car is on the parking space to judge whether the current position has the car or not by using the machine vision and an image processing technology.
There is currently no effective solution to the above-described problems in the related art.
Disclosure of Invention
The embodiment of the invention provides a method and a device for identifying a parking space state, a storage medium and an electronic device, which at least solve the problem of low accuracy in identifying the state of a platform of a logistics park by setting a mark point and directly identifying the parking space in the related art.
According to one embodiment of the invention, there is provided a parking space state identification method, including: acquiring a plurality of images shot in a preset area; the preset area is used for parking a vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks; identifying the parking space occupation state in a preset area in the images and the number of marks arranged on the anti-collision rod; 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 marks.
According to another embodiment of the present invention, there is provided an identification device for a parking space status, including: the acquisition module is used for acquiring a plurality of images shot in a preset area; the preset area is used for parking a vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks; the identification module is used for identifying the parking space occupation state in a preset area in the images and the number of the marks arranged on the anti-collision rod; 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 marks.
According to a further embodiment of the invention, there is also provided a storage medium having stored therein a computer program, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
According to a further embodiment of the invention, there is also provided an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
According to the invention, the parking space occupation state in the preset area and the number of the marks on the anti-collision rod are used for identifying, so that the accuracy of identifying the parking space state is improved, and the problem of low accuracy of identifying the state of the platform of the logistics park by setting the mark points 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 embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a method of identifying parking spot status according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a sample image in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram II of a sample image according to an embodiment of the invention;
FIG. 4 is a schematic diagram III of a sample image according to an embodiment of the invention;
FIG. 5 is a schematic diagram fourth of a sample image according to an embodiment of the invention;
FIG. 6 is a schematic diagram fifth of a sample image according to an embodiment of the invention;
FIG. 7 is a schematic diagram six 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 drawings in conjunction with embodiments. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects 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, fig. 1 is a flowchart of a method for identifying a parking space state according to an embodiment of the present invention, and as shown in fig. 1, the flowchart includes the following steps:
step S102, acquiring a plurality of images shot in a preset area; the preset area is used for parking the vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks;
step S104, recognizing the parking space occupation state in a preset area in a plurality of images and the number of marks arranged on the anti-collision rod;
step 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 marks.
Through the steps S102 to S106, the accuracy of identifying the parking space state 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 accuracy of identifying the state of the logistics park platform by setting the mark points and directly identifying the parking spaces is solved.
Optionally, in this embodiment, the manner of identifying the occupancy state of the parking spaces in the preset area in the plurality of images and the number of the bumper beam marks set in step S104 may further be: identifying the parking space occupation state in a preset area in a plurality of images and the number of the anti-collision bar marks through the trained network model; the trained network model is obtained by training the primary network model by using a plurality of sample images; 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 unoccupied state; the anti-collision rod in the sample image is marked by two different colors, and the two colors marked continuously form a mark.
Alternatively, the sample image is a matted image in this embodiment.
For the step S104, taking the dock with the preset area as 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 the state recognition, the parking space area is subjected to matting and perspective transformation, as shown in fig. 3. And for the parking space occupation state in the sample image, the state comprises: a fully occupied state, an unoccupied state, and an unoccupied state; the three states in the logistics park are shown in figure 4; the following are respectively from left to right: unoccupied state, fully occupied state. In a specific embodiment, a deep learning method is utilized, the specific method is that the modified VGG network trains sample image data to obtain a classification network model of parking space states, and then the network model is utilized to identify states of a map to be detected.
Optionally, in this embodiment, the identifying manner for the parking space occupation state includes at least one of the following:
(1) Under the condition that the parking space occupation state of images exceeding a first preset number in the plurality of images is recognized as a fully occupied state through the trained network model, a recognition result for indicating that the preset area is occupied is obtained;
taking a preset area as a logistics platform as an example, the process illustrates that the platform is in an occupied state 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 images exceeding a first preset number in the plurality of images is identified as an unoccupied state through the trained network model, an identification result for indicating that a preset area is unoccupied is obtained;
taking a preset area as a logistics platform as an example, the process refers to a state that the platform is unoccupied before the plurality of images are acquired.
(3) Under the condition that the parking space occupation state of more than a first preset number of images in the plurality of images is recognized as a change process from a fully occupied state to a not occupied state through the trained network model, a recognition result for indicating that a preset area is not occupied is obtained;
taking a preset area as a logistics dock as an example, the process indicates that the vehicle of the dock is leaving the dock.
(4) 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 the unoccupied state to the fully occupied state through the trained network model, an identification result for indicating that the preset area is occupied is obtained.
Taking a preset area as a logistics platform as an example, the process indicates that a vehicle is entering the platform.
It should be noted that, the first preset number may be a corresponding value according to the actual situation, for example, the plurality of images are 50, and the first preset number may be 40 or other values. The purpose is to be able to avoid false recognition.
Optionally, in this embodiment, the identifying manner for the number of identifiers includes at least one of:
(1) Obtaining a recognition result for indicating that the bumper is not blocked under the condition that the number of the marks consisting of two continuous colors in the plurality of images exceeds a second preset number through the trained network model;
(2) And obtaining a recognition result for indicating that the bumper bar is blocked under the condition that the number of the marks consisting of the two colors which are continuously marked in the plurality of images through the trained network model does not exceed the second preset number.
It should be noted that, the second preset number of values may be correspondingly valued according to the actual situation.
In the specific implementation manner of this embodiment, taking the logistics park platform as an example, the detection area of the bumper bar needs to be known in advance by using a manual labeling manner, as shown in fig. 5; in order to facilitate the state recognition, the bumper bar region is subjected to a matting process as shown in fig. 6. Further, whether the bumper is shielded or not is identified, and the detection target is a yellow-black block and/or a black-yellow block. The deep learning method is utilized, the optimized YOLOV3 network is specifically used for training corresponding image data, a relevant detection network and a detection model are obtained, and then the network and the model are utilized for identifying the image to be detected, and the identification effect is shown in fig. 7.
Optionally, in this embodiment, the manner of 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 in step S106 includes the following manners:
(1) Under the condition that the identification result indicates that the preset area is occupied and the anti-collision rod is blocked, determining that the parking space state is a normal occupied state;
(2) Under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision rod is not blocked, determining that the parking space state is an idle state;
(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 rod 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 bumper is blocked.
Through the four modes, the parking space state normal occupation state and the idle state can be identified, and the abnormal state can be identified, namely, the embodiment utilizes the article of the on-site crash bar, and the method of combining detection is used for identifying whether the crash bar is shielded or not, so that the final state of the platform can be output more accurately according to the shielding condition of the crash bar and the parking space occupation condition, and after the two identification states are combined, a plurality of abnormal conditions can be well resisted, so that the accuracy is high and the robustness is good.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform 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 for implementing the foregoing embodiments and preferred embodiments, and is not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, 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 apparatus according to an embodiment of the present invention, as shown in fig. 8, including:
(1) An acquisition module 82, configured to acquire a plurality of images captured in a preset area; the preset area is used for parking the vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks;
(2) The identification module 84 is used for identifying the parking space occupation state in a preset area in the images and the number of the marks arranged on the bumper;
(3) The determining module 86 is configured to determine a 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 identifying module 84 is further configured to identify, by using the trained network model, a parking space occupation state in a preset area in the plurality of images and the number of the anti-collision bar identifiers; the trained network model is obtained by training the primary network model by using a plurality of sample images; 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 unoccupied 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 identifying manner in this embodiment includes at least one of the following:
under the condition that the parking space occupation state of images exceeding a first preset number in the plurality of images is recognized as a fully occupied state through the trained network model, a recognition result for indicating that the preset area is occupied is obtained;
under the condition that the parking space occupation state of images exceeding a first preset number in the plurality of images is identified as an unoccupied state through the trained network model, an identification result for indicating that a preset area is unoccupied is obtained;
under the condition that the parking space occupation state of more than a first preset number of images in the plurality of images is recognized as a change process from a fully occupied state to a not occupied state through the trained network model, a recognition result for indicating that a preset area is not occupied is obtained;
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 the unoccupied state to the fully occupied state through the trained network model, an identification result for indicating that the preset area is occupied is obtained.
Optionally, the identifying manner of the number of the identifiers in the present embodiment includes at least one of the following:
obtaining a recognition result for indicating that the bumper is not blocked under the condition that the number of the marks consisting of two continuous 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 bumper bar is blocked under the condition that the number of the marks consisting of the two colors which are continuously marked in the plurality of images through the trained network model does not exceed the second preset number.
Optionally, the determining module 86 in the present 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 identification result indicates that the preset area is occupied and the anti-collision rod 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 rod 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 identification result indicates that the preset area is occupied and the anti-collision rod is not blocked; and a fourth determining unit for determining that the parking space state is an abnormal state in the case where the recognition result indicates that the preset area is unoccupied and the bumper has been blocked.
Note that, the sample image in this embodiment is an image subjected to the 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 vehicle position state is identified to be in an abnormal state.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.
Example 3
An embodiment of the invention also provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
Alternatively, in the present embodiment, the above-described storage medium may be configured to store a computer program for performing the steps of:
s1, acquiring a plurality of images shot in a preset area; the preset area is used for parking the vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks;
s2, identifying the parking space occupation state in a preset area in a plurality of images and the number of marks arranged on the anti-collision rod;
s3, 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 marks.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
An embodiment of the invention also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s1, acquiring a plurality of images shot in a preset area; the preset area is used for parking the vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks;
s2, identifying the parking space occupation state in a preset area in a plurality of images and the number of marks arranged on the anti-collision rod;
s3, 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 marks.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and optional implementations, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a memory device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The method for identifying the parking space state is characterized by comprising the following steps:
acquiring a plurality of images shot in a preset area; the preset area is used for parking a vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks;
identifying the parking space occupation state in a preset area in the images and the number of marks arranged on the anti-collision rod;
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 marks;
the anti-collision rod comprises two marks with different colors, wherein the two colors of the continuous marks form a mark;
wherein, the identification mode for identifying the number of the marks arranged on the bumper bar comprises at least one of the following:
obtaining a recognition result for indicating that the bumper is not blocked under the condition that the number of the marks consisting of two continuous colors in the plurality of images exceeds a second preset number through the trained network model;
obtaining a recognition result for indicating that the bumper bar is blocked under the condition that the number of the marks consisting of two colors continuously marked in the plurality of images does not exceed a second preset number through the trained network model;
the 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 marks comprises the following steps:
under the condition that the identification result indicates that the preset area is occupied and the anti-collision rod is blocked, determining that the parking space state is a normal occupied state;
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 rod 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 rod is not blocked;
and under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision rod is blocked, determining that the parking space state is an abnormal state.
2. The method of claim 1, wherein the identifying the occupancy status of the parking space in the predetermined area of the plurality of images and the number of the marks provided on the bumper bar comprises:
identifying the parking space occupation state in a preset area in the images and the number of the anti-collision rod marks through the trained network model; the trained network model is obtained by training a primary network model by using a plurality of sample images; 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 unoccupied state; the anti-collision rod in the sample image is marked by two different colors, and the two colors marked continuously form the mark.
3. The method of claim 2, wherein the means for identifying the occupancy state comprises at least one of:
under the condition that the parking space occupation state of images exceeding a first preset number in the plurality of images is recognized as a fully occupied state through the trained network model, a recognition result for indicating that the preset area is occupied is obtained;
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 the unoccupied state through the trained network model, an identification result for indicating that the preset area is unoccupied is obtained;
under the condition that the parking space occupation state of more than a first preset number of images in the plurality of images is recognized as a change process from a fully occupied state to a not occupied state through the trained network model, a recognition result for indicating that the preset area is not occupied is obtained;
and under the condition that the parking space occupation state of the images exceeding the first preset number in the images is recognized as a change process from the unoccupied state to the fully occupied state through the trained network model, a recognition result for indicating that the preset area is occupied is obtained.
4. The method according to claim 1, wherein the method further comprises:
and sending a reminding message under the condition that the parking space state is identified to be an abnormal state.
5. A method as claimed in claim 2, wherein the sample image is a matted image.
6. The utility model provides an identification device of parking stall state which characterized in that includes:
the acquisition module is used for acquiring a plurality of images shot in a preset area; the preset area is used for parking a vehicle, and an anti-collision rod used for collision prevention is arranged in the preset area; the anti-collision rod is provided with a plurality of marks;
the identification module is used for identifying the parking space occupation state in a preset area in the images and the number of the marks arranged on the anti-collision rod;
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 marks;
the anti-collision rod comprises two marks with different colors, wherein the two colors of the continuous marks form a mark;
wherein, the identification mode for identifying the number of the marks arranged on the bumper bar comprises at least one of the following:
obtaining a recognition result for indicating that the bumper is not blocked under the condition that the number of the marks consisting of two continuous colors in the plurality of images exceeds a second preset number through the trained network model;
obtaining a recognition result for indicating that the bumper bar is blocked under the condition that the number of the marks consisting of two colors continuously marked in the plurality of images does not exceed a second preset number through the trained network model;
the 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 marks comprises the following steps:
under the condition that the identification result indicates that the preset area is occupied and the anti-collision rod is blocked, determining that the parking space state is a normal occupied state;
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 rod 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 rod is not blocked;
and under the condition that the identification result indicates that the preset area is unoccupied and the anti-collision rod is blocked, determining that the parking space state is an abnormal state.
7. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 1 to 5 when run.
8. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of the claims 1 to 5.
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| 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 |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104282169A (en) * | 2014-09-29 | 2015-01-14 | 四川川大智胜软件股份有限公司 | Embedded integration parking space detector and detecting method thereof |
| EP3038014A1 (en) * | 2014-12-22 | 2016-06-29 | Orange | Device and method for detecting an occupancy state of a location |
| CN106652551A (en) * | 2016-12-16 | 2017-05-10 | 浙江宇视科技有限公司 | Parking stall detection method and device |
| WO2017179205A1 (en) * | 2016-04-15 | 2017-10-19 | 三菱電機株式会社 | Display control device for parking assistance, and display control method for parking assistance |
| CN107909850A (en) * | 2017-12-13 | 2018-04-13 | 武汉泰康翔科技股份有限公司 | A kind of parking garage management system and method |
| CN108806316A (en) * | 2018-06-14 | 2018-11-13 | 郑州云海信息技术有限公司 | A kind of parking management system of vehicle |
| CN108921955A (en) * | 2018-06-28 | 2018-11-30 | 西安艾润物联网技术服务有限责任公司 | Parking process method, apparatus and computer readable storage medium |
| CN109255947A (en) * | 2018-06-29 | 2019-01-22 | 深圳昂云鼎科技有限公司 | A kind of parking stall management method, apparatus and terminal device |
| CN110096975A (en) * | 2019-04-17 | 2019-08-06 | 北京筑梦园科技有限公司 | A kind of parking space state recognition methods, equipment and system |
| CN110245544A (en) * | 2018-09-26 | 2019-09-17 | 浙江大华技术股份有限公司 | A kind of method and device of determining dead ship condition |
| WO2020001263A1 (en) * | 2018-06-25 | 2020-01-02 | 杭州海康威视数字技术股份有限公司 | Parking space recognition |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111028535B (en) * | 2018-10-09 | 2021-04-27 | 杭州海康威视数字技术股份有限公司 | Parking space detection device and method and parking space management system |
-
2020
- 2020-04-17 CN CN202010307126.9A patent/CN111507269B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104282169A (en) * | 2014-09-29 | 2015-01-14 | 四川川大智胜软件股份有限公司 | Embedded integration parking space detector and detecting method thereof |
| EP3038014A1 (en) * | 2014-12-22 | 2016-06-29 | Orange | Device and method for detecting an occupancy state of a location |
| WO2017179205A1 (en) * | 2016-04-15 | 2017-10-19 | 三菱電機株式会社 | Display control device for parking assistance, and display control method for parking assistance |
| CN106652551A (en) * | 2016-12-16 | 2017-05-10 | 浙江宇视科技有限公司 | Parking stall detection method and device |
| CN107909850A (en) * | 2017-12-13 | 2018-04-13 | 武汉泰康翔科技股份有限公司 | A kind of parking garage management system and method |
| CN108806316A (en) * | 2018-06-14 | 2018-11-13 | 郑州云海信息技术有限公司 | A kind of parking management system of vehicle |
| WO2020001263A1 (en) * | 2018-06-25 | 2020-01-02 | 杭州海康威视数字技术股份有限公司 | Parking space recognition |
| CN108921955A (en) * | 2018-06-28 | 2018-11-30 | 西安艾润物联网技术服务有限责任公司 | Parking process method, apparatus and computer readable storage medium |
| CN109255947A (en) * | 2018-06-29 | 2019-01-22 | 深圳昂云鼎科技有限公司 | A kind of parking stall management method, apparatus and terminal device |
| CN110245544A (en) * | 2018-09-26 | 2019-09-17 | 浙江大华技术股份有限公司 | A kind of method and device of determining dead ship condition |
| CN110096975A (en) * | 2019-04-17 | 2019-08-06 | 北京筑梦园科技有限公司 | A kind of parking space state recognition methods, equipment and system |
Non-Patent Citations (1)
| Title |
|---|
| 郑志锋.基于深度学习的智能停车场车位查询系统.《计算机系统应用》.2019,全文. * |
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