Intelligent parking space management system, method and equipment based on camera and medium

Abstract

The invention provides an intelligent parking space management system, method and device based on a camera and a medium, and belongs to the technical field of intelligent parking management. The system comprises a camera module used for collecting a parking lot image at a preset frequency and transmitting the parking lot image through a network; the image analyzing and processing module is used for identifying parking space boundaries through edge detection and a semantic segmentation algorithm according to the parking lot images, determining parking space areas, counting the number of parking spaces and judging parking space states and legality of parked vehicles through a target detection and license plate recognition algorithm; the data storage module is used for storing parking space state information and related vehicle information; and the management terminal is used for displaying the parking space state information in real time and responding to an abnormal alarm. According to the invention, images are acquired through the camera, the images are analyzed and processed by using algorithms such as edge detection, semantic segmentation, target detection and license plate recognition, and data storage and management terminal interaction are combined, so that accurate management of parking spaces, timely early warning of abnormal conditions and legal vehicle recognition are realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent parking management technology, and more specifically relates to a camera-based intelligent parking space management system, method, device and medium. Background Technology

[0002] With the continuous surge in urban car ownership, the contradiction between parking space supply and demand is becoming increasingly acute. Efficient and precise parking space management has become a key link in improving traffic operation efficiency and property management level. At present, although various parking space management systems have alleviated management pressure to some extent, they still have significant technical limitations in practical applications and are difficult to meet the refined management needs of modern parking lots.

[0003] Parking management systems based on geomagnetic sensors were among the earliest technologies used, but their core drawback lies in insufficient detection accuracy. These systems are susceptible to interference from environmental factors such as soil moisture and surrounding metal objects, often resulting in misjudgments of parking space occupancy. More importantly, they can only achieve a binary judgment of "whether a vehicle is present," failing to obtain crucial information such as license plate numbers and vehicle models, rendering them completely ineffective in scenarios requiring precise data support, such as fee management and vehicle traceability.

[0004] The ultrasonic sensor solution also has significant drawbacks. Limited by its technical principles, its detection distance is strictly restricted. In large parking lots or densely packed parking environments, interference from signal reflections and superposition can easily occur, leading to distorted detection results. Similar to geomagnetic sensors, this technology cannot collect detailed vehicle information, making it difficult to meet the diverse management needs of complex parking lots. For example, it cannot distinguish between temporary and permanent vehicles, nor can it achieve targeted management of specific parking spaces.

[0005] While existing camera-based parking management systems have made breakthroughs in information collection, they still haven't solved core technical pain points. Some systems can only achieve basic license plate recognition, relying on simple image analysis to determine parking space occupancy status; other systems can identify parking space and vehicle information, but are limited by the efficiency of image processing algorithms and data transmission mechanisms, resulting in extremely poor real-time performance. This prevents car owners from obtaining accurate parking space information in a timely manner, and makes it difficult for parking lots to achieve dynamic scheduling. Summary of the Invention

[0006] To address the above problems, the present invention aims to provide a camera-based intelligent parking space management system, method, device, and medium. By acquiring images through a camera, performing image analysis and processing using algorithms such as edge detection, semantic segmentation, object detection, and license plate recognition, and combining data storage and management terminal interaction, the system achieves precise parking space management, timely early warning of abnormal situations, and identification of legitimate vehicles.

[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, embodiments of this application provide a camera-based intelligent parking management system, including: a camera module, an image analysis and processing module, a data storage module, and a management terminal. The image analysis and processing module is connected to the camera module, the data storage module, and the management terminal via a network. The management terminal is connected to the data storage module via a network. The camera module is used to capture parking lot images at a preset frequency and transmit them over the network; The image analysis and processing module is used to identify parking space boundaries, determine parking space areas, and count the number of parking spaces based on parking lot images using edge detection and semantic segmentation algorithms. It also uses object detection and license plate recognition algorithms to determine the status of parking spaces and the legality of parked vehicles. The data storage module is used to store parking space status information and related vehicle information; The management terminal is used to display parking space status information in real time and respond to abnormal alarms.

[0008] In an optional implementation, when the parking lot is an indoor parking lot, the camera modules are installed on the ceiling or wall at intervals of 10 to 15 meters. When the parking lot is an outdoor parking lot, the camera module is installed around the parking lot and at preset key locations; The camera module captures parking lot images at 1 to 2 frames per second and transmits the parking lot images to the image analysis and processing module via a wired or wireless network. The resolution of the camera module is no less than 1080P.

[0009] In an optional implementation, the image analysis and processing module includes: The parking space quantity recognition unit is specifically used for: The parking lot image is sequentially processed by grayscale conversion and Gaussian filtering for noise reduction. Based on the processed parking lot image, the Canny edge detection algorithm is used to identify the edge information of the parking space boundary and extract the parking space boundary line. Based on the parking space boundary lines, the parking space areas are divided and numbered using the Mask R-CNN semantic segmentation model. The number of parking spaces is then counted and updated to the data storage module in real time.

[0010] In an optional implementation, the image analysis and processing module further includes: The parking space status recognition unit is specifically used for: Based on parking lot images, the YOLO algorithm is used to detect targets in the parking space area. When a vehicle is detected in the parking space area, the vehicle feature information is extracted, including the vehicle outline. By comparing the relative position of the vehicle outline with the parking space boundary line, it is determined whether the vehicle is parked in accordance with regulations; if not, an early warning message is sent to the management terminal. The built-in timestamp function records the parking time of vehicles. If the parking time exceeds the preset duration, it is judged as an abnormal parking space and an early warning message is sent to the management terminal. The OCR recognition algorithm identifies the vehicle's license plate number and compares it with the license plate information in the parking lot's database to determine whether the vehicle is legitimate. If not, it sends an alarm message to the management terminal.

[0011] In one optional implementation, the target detection in the parking space area using the YOLO algorithm includes: After detecting an object within the parking space area using the YOLO algorithm, the object's outline, size, and texture are matched with a pre-stored vehicle model; if a match is found, the object is preliminarily identified as a suspected vehicle. Multiple frames of parking lot images are continuously acquired, suspected vehicles are identified, and the movement trajectory of the suspected vehicles is tracked. The movement trajectory and the final movement state of the suspected vehicles are used to determine whether they match the characteristics of a vehicle. If they do, the suspected vehicle is identified as a vehicle; otherwise, it is considered a false positive.

[0012] In an optional implementation, the data storage module is specifically used for: Store parking lot images, number of parking spaces, parking space status, license plate numbers, and corresponding vehicle entry and exit times; Real-time backup of stored information, and data recovery of fields in the event of data anomalies; It interacts with the image analysis and processing module in real time to provide parking space status information.

[0013] In one optional implementation, the management terminal uses a computer, tablet, or mobile device to read the number of parking spaces, parking space status, license plate number, and corresponding vehicle entry and exit time from the data storage module, and generate parking space usage statistics reports and vehicle entry and exit record reports; the management terminal is equipped with a car owner interface to support mobile APP querying real-time parking space information and reservation operations.

[0014] Secondly, embodiments of this application also provide a camera-based intelligent parking space management method, including: The camera module captures parking lot images at a preset frequency and transmits them to the image analysis and processing module via the network. The image analysis and processing module uses edge detection and semantic segmentation algorithms to identify parking space boundaries, determine parking space areas, and count the number of parking spaces based on the parking lot image. It also uses object detection and license plate recognition algorithms to determine the status of parking spaces and the legality of parked vehicles. The data storage module stores parking space status information and related vehicle information. The management terminal displays parking space status information in real time and responds to abnormal alarms.

[0015] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the camera-based intelligent parking space management method described in any of the above descriptions.

[0016] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the camera-based intelligent parking space management method described in any of the above embodiments.

[0017] As can be seen from the above technical solutions, the present invention has the following advantages: The camera-based intelligent parking management system provided in this application utilizes a high-resolution camera module for scientific deployment and efficient image acquisition. The image analysis and processing module employs various advanced algorithms to accurately identify parking space information, determine vehicle status and legality, and combines this with information storage from the data storage module and real-time monitoring from the management terminal. This not only enables precise parking space management and real-time monitoring of parking space dynamics but also provides timely warnings of abnormal situations and identification of illegal vehicles, effectively improving parking lot management efficiency and security. Simultaneously, it offers convenient parking space information query services to car owners, balancing the needs of both management and users.

[0018] This application utilizes advanced image recognition and deep learning algorithms to accurately count the number of parking spaces in various types of parking lots, unaffected by complex indoor layouts or outdoor environments. This allows parking lot managers to have a real-time, accurate grasp of the total number of parking spaces and to rationally plan parking resources.

[0019] This application can not only determine whether a vehicle is parked properly, but also identify whether there is abnormally long-term occupancy and whether the vehicle is legal. This effectively maintains the order of the parking lot. For the management of proper parking, it can reduce the situation where vehicles parked haphazardly affect the passage of other vehicles; for monitoring abnormally long-term occupancy, it can rationally allocate scarce parking space resources and prevent individual vehicles from occupying them unreasonably; and for identifying illegal vehicles, it enhances the security of the parking lot and protects the rights and interests of legitimate users.

[0020] This application effectively improves the accuracy of vehicle recognition through precise vehicle feature comparison and multi-frame image analysis and tracking.

[0021] This application eliminates the need for extensive manpower in daily parking space management, reducing labor costs. Furthermore, it can be integrated with other intelligent transportation systems or parking management systems to enable functions such as parking space reservation and intelligent guidance, aligning with the development trend of smart cities and providing strong support for the construction of a future intelligent transportation ecosystem. Simultaneously, the large amount of data accumulated by the data storage unit can be used for in-depth data analysis to uncover parking lot usage patterns, providing data support for optimizing parking lot layout and adjusting charging strategies, further improving the overall management efficiency of parking lots. Attached Figure Description

[0022] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of the structure of the camera-based intelligent parking management system provided in this application.

[0024] Figure 2 A flowchart illustrating the camera-based intelligent parking space management method provided in this application.

[0025] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0026] The various embodiments of this disclosure will be described more fully in the following detailed description of the specific system architecture and functions of the camera-based intelligent parking management system. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0027] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figure 1 The diagram shown is a schematic representation of a camera-based intelligent parking management system in a specific embodiment. The system includes a camera module, an image analysis and processing module, a data storage module, and a management terminal. The image analysis and processing module is connected to the camera module, the data storage module, and the management terminal via a network. The management terminal is connected to the data storage module via a network.

[0030] The camera module is used to capture images of the parking lot at a preset frequency and transmit them over the network.

[0031] Specifically, multiple camera modules are installed in appropriate locations within the parking lot. For indoor parking lots, depending on the layout and size, a camera module is typically installed at regular intervals (e.g., 10-15 meters) high on the ceiling or walls to ensure each parking space is clearly captured. For outdoor parking lots, considering varying lighting conditions and monitoring ranges, camera modules with automatic exposure and zoom functions can be used, strategically distributed around the perimeter and key locations to ensure comprehensive monitoring of all parking spaces. The camera modules must have high resolution, at least 1080P, to obtain clear image information.

[0032] In addition, the camera module captures parking lot images at 1 to 2 frames per second and transmits the parking lot images to the image analysis and processing module via (e.g., Ethernet) or wireless networks (e.g., high-speed networks such as 5G, WiFi 6, etc.).

[0033] The image analysis and processing module is used to identify parking space boundaries, determine parking space areas, and count the number of parking spaces based on parking lot images using edge detection and semantic segmentation algorithms. It also uses object detection and license plate recognition algorithms to determine the status of parking spaces and the legality of parked vehicles.

[0034] In a specific implementation, the image analysis and processing module includes a parking space quantity recognition unit and a parking space status recognition unit.

[0035] The parking space quantity recognition unit is specifically used for: The parking lot image is sequentially processed by grayscale conversion and Gaussian filtering for noise reduction. Based on the processed parking lot image, the Canny edge detection algorithm is used to identify the edge information of the parking space boundary and extract the parking space boundary line. Based on the parking space boundary lines, the parking space areas are divided and numbered using the Mask R-CNN semantic segmentation model. The number of parking spaces is then counted and updated to the data storage module in real time.

[0036] For example, the parking space quantity recognition unit specifically executes the following process: Image preprocessing: Upon receiving the parking lot image, the image is first converted to grayscale to simplify subsequent processing steps. Then, Gaussian filtering and other noise reduction algorithms are used to remove noise generated during camera capture or transmission, improving image quality.

[0037] Parking space boundary recognition: Using edge detection technologies such as the Canny edge detection algorithm, parking space boundary lines are extracted from parking lot images. By setting an appropriate threshold, the edge information of the parking space boundaries is accurately identified.

[0038] Parking space area segmentation and counting: Based on deep learning semantic segmentation algorithms, such as the Mask R-CNN model, the image of a parking lot after edge detection is processed to accurately segment each parking space area. During training, this model uses a large amount of image data from different parking lots, covering various parking space layouts and scenes, and can accurately identify different types of parking spaces. After segmenting the parking space areas, a simple counting algorithm is used to count the number of parking spaces and assign them numbers, and the parking space count information is updated to the data storage module in real time.

[0039] The parking space status recognition unit is specifically used for: Based on parking lot images, the YOLO algorithm is used to detect targets in the parking space area. When a vehicle is detected in the parking space area, the vehicle feature information is extracted. The vehicle feature information includes vehicle outline, vehicle color, and vehicle model. By comparing the relative position of the vehicle outline with the parking space boundary line, it is determined whether the vehicle is parked in accordance with regulations; if not, an early warning message is sent to the management terminal. The built-in timestamp function records the parking time of vehicles. If the parking time exceeds the preset duration, it is judged as an abnormal parking space and an early warning message is sent to the management terminal. The OCR recognition algorithm identifies the vehicle's license plate number and compares it with the license plate information in the parking lot's database to determine whether the vehicle is legitimate. If not, it sends an alarm message to the management terminal.

[0040] For example, the parking space status recognition unit executes the following process: Object detection: Deep learning-based object detection algorithms, such as the YOLO (You Only Look Once) series, are used to detect objects within the parking space area. This algorithm can quickly and accurately identify target objects such as vehicles and people. When a vehicle is detected within the parking space area, its feature information, such as its outline, color, and model, is further extracted.

[0041] Parking location analysis: By analyzing the relative position of the vehicle's outline to the parking space boundary line, it determines whether the vehicle is parked correctly. For example, if most of the vehicle's outline extends beyond the parking space boundary line, it is considered improperly parked, and this information is recorded in the data storage unit. Simultaneously, a reminder can be sent to parking lot management personnel via the management terminal.

[0042] Occupancy Time Recording and Anomaly Detection: The system uses timestamps to record the time a vehicle enters a parking space. When a vehicle leaves the space, the system calculates the time the vehicle remained in the space. This time is compared to a preset normal time range. If the time exceeds the normal range, it is considered an abnormally long occupancy, and this is recorded in the data storage unit and the administrator is notified.

[0043] Legality Verification: Combining license plate recognition technology, the system uses OCR (Optical Character Recognition) algorithms to identify vehicle license plates. The identified license plate number is compared with the parking lot's database (stored in a data storage module) to determine if the vehicle belongs to a legitimate user. If it is an illegal vehicle, the system issues an alarm and notifies management personnel.

[0044] In the parking space status recognition unit, vehicle target detection is achieved through a deep learning-based object detection algorithm to confirm the presence of vehicles. Specifically, this includes: After detecting an object within the parking space area using the YOLO algorithm, the object's outline, size, and texture are matched with a pre-stored vehicle model; if a match is found, the object is preliminarily identified as a suspected vehicle. Multiple frames of parking lot images are continuously acquired, suspected vehicles are identified, and the movement trajectory of the suspected vehicles is tracked. The movement trajectory and the final movement state of the suspected vehicles are used to determine whether they match the characteristics of a vehicle. If they do, the suspected vehicle is identified as a vehicle; otherwise, it is considered a false positive.

[0045] For example, the vehicle existence verification process is as follows: Vehicle Feature Comparison: When an object is detected in the parking space area, its outline, size, texture, and other features are compared with pre-existing vehicle feature models in the system. These vehicle feature models are obtained through analysis and learning from a large amount of image data of different types of vehicles, and possess high accuracy and versatility. Only when the object's features highly match the vehicle feature model is it preliminarily identified as a suspected vehicle.

[0046] Multi-frame image analysis and motion trajectory tracking: To avoid misjudgment, this unit employs multi-frame image analysis technology. First, multiple frames of images are acquired consecutively, and the motion trajectory of objects suspected to be vehicles is tracked. If the object's motion trajectory in the multi-frame images matches the characteristics of a vehicle entering or exiting a parking space, and it remains relatively stationary within the parking space, then it is ultimately determined that a vehicle is parked in the current parking space; conversely, if the object's motion trajectory is chaotic or does not match the characteristics of vehicle motion, it is judged as a misjudgment, and the possibility of the object being a vehicle is ruled out.

[0047] The data storage module is used to store parking space status information and related vehicle information.

[0048] In a specific implementation, the data storage module uses a high-capacity hard drive or cloud storage device to store image data captured by the camera, the number of parking spaces after analysis and processing, the parking space status, and vehicle-related information (such as license plate number, entry and exit time, etc.). The data storage module has data backup and recovery functions to prevent data loss and can quickly interact with the image analysis and processing module and the management terminal.

[0049] The management terminal is used to display parking space status information in real time and respond to abnormal alarms.

[0050] In a specific implementation, the management terminal can be a computer, tablet, or mobile phone, connected to the image analysis and processing module and data storage module via a network. Managers can use the terminal to view the number and status of parking spaces in real time and handle any anomalies promptly. Simultaneously, the terminal can generate various reports based on the acquired information, such as parking space usage statistics and vehicle entry / exit records, facilitating data analysis and decision-making for managers. The terminal also includes a driver interface, allowing users to connect via a mobile app to obtain real-time parking information and perform actions such as parking space reservations.

[0051] In this embodiment, the system utilizes 1080P or higher resolution camera modules deployed at scientific intervals to capture parking lot images at a frequency of 1-2 frames per second. It identifies parking space boundaries, divides areas, and counts the number of spaces using Canny edge detection and MaskR-CNN semantic segmentation algorithms. YOLO algorithms are used for target detection to determine the standardization of vehicle parking and whether the dwell time is abnormal. OCR recognition algorithms are combined to identify license plates and compare them with a database to determine vehicle legitimacy. Combined with the information storage module and the real-time response of the management terminal, the system achieves precise and intelligent management of parking spaces. It can promptly warn of abnormal situations and identify illegal vehicles, improving parking lot management efficiency and security, and providing car owners with convenient parking space query services.

[0052] like Figure 2 As shown, the following are embodiments of the camera-based intelligent parking space management method provided in this disclosure. This method and the camera-based intelligent parking space management system in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the camera-based intelligent parking space management method, please refer to the embodiments of the camera-based intelligent parking space management system described above.

[0053] A camera-based intelligent parking space management method includes the following steps: S1: The camera module captures parking lot images at a preset frequency and transmits them to the image analysis and processing module via the network.

[0054] S2: The image analysis and processing module uses edge detection and semantic segmentation algorithms to identify parking space boundaries, determine parking space areas, and count the number of parking spaces based on the parking lot image. It also uses object detection and license plate recognition algorithms to determine the status of parking spaces and the legality of parked vehicles.

[0055] S3: The data storage module stores parking space status information and related vehicle information.

[0056] S4: Display parking space status information in real time and respond to abnormal alarms through the management terminal.

[0057] The camera-based intelligent parking space management method provided in this embodiment acquires images through a camera, processes them using algorithms such as edge detection, semantic segmentation, object detection, and license plate recognition, and combines them with a data storage and management terminal to achieve precise parking space management, abnormal warnings, and identification of legitimate vehicles, thereby improving the efficiency and security of parking lot management and providing convenient services for car owners.

[0058] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0059] The camera-based intelligent parking management method provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0060] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0061] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0062] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0063] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0064] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0065] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0066] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0067] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0068] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0069] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0070] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0071] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0072] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0073] The aforementioned electronic device realizes the intelligent parking space management method based on cameras in this application. It acquires images through a camera module, processes them using algorithms such as Canny edge detection, MaskR-CNN semantic segmentation, YOLO object detection, and OCR license plate recognition, and combines data storage and management terminal interaction to achieve the beneficial effects of intelligent parking space management, timely warning of anomalies, improved efficiency and safety, and convenience for car owners.

[0074] The storage medium provided in this application stores a program product capable of implementing a camera-based intelligent parking space management method.

[0075] Camera-based intelligent parking management methods include: The camera module captures parking lot images at a preset frequency and transmits them to the image analysis and processing module via the network. The image analysis and processing module uses edge detection and semantic segmentation algorithms to identify parking space boundaries, determine parking space areas, and count the number of parking spaces based on the parking lot image. It also uses object detection and license plate recognition algorithms to determine the status of parking spaces and the legality of parked vehicles. The data storage module stores parking space status information and related vehicle information. The management terminal displays parking space status information in real time and responds to abnormal alarms. In some possible implementations, the camera-based intelligent parking space management method of this disclosure can be implemented as a program product that includes program code. When the program product is run on a terminal device, the program code is used to cause the terminal device to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0076] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A camera-based intelligent parking space management system, characterized in that, include: The system includes a camera module, an image analysis and processing module, a data storage module, and a management terminal. The image analysis and processing module is connected to the camera module, the data storage module, and the management terminal via a network. The management terminal is connected to the data storage module via a network. The camera module is used to capture parking lot images at a preset frequency and transmit them over the network; The image analysis and processing module is used to identify parking space boundaries, determine parking space areas, and count the number of parking spaces based on parking lot images using edge detection and semantic segmentation algorithms. It also uses object detection and license plate recognition algorithms to determine the status of parking spaces and the legality of parked vehicles. The data storage module is used to store parking space status information and related vehicle information; The management terminal is used to display parking space status information in real time and respond to abnormal alarms.

2. The camera-based intelligent parking management system according to claim 1, characterized in that: When the parking lot is an indoor parking lot, the camera modules are installed on the ceiling or wall at intervals of 10 to 15 meters; When the parking lot is an outdoor parking lot, the camera module is installed around the parking lot and at preset key locations; The camera module captures parking lot images at 1 to 2 frames per second and transmits the parking lot images to the image analysis and processing module via a wired or wireless network. The resolution of the camera module is no less than 1080P.

3. The camera-based intelligent parking management system according to claim 1, characterized in that, The image analysis and processing module includes: The parking space quantity recognition unit is specifically used for: The parking lot image is sequentially processed by grayscale conversion and Gaussian filtering for noise reduction. Based on the processed parking lot image, the Canny edge detection algorithm is used to identify the edge information of the parking space boundary and extract the parking space boundary line. Based on the parking space boundary lines, the parking space areas are divided and numbered using the MaskR-CNN semantic segmentation model. The number of parking spaces is then counted and updated to the data storage module in real time.

4. The camera-based intelligent parking management system according to claim 3, characterized in that, The image analysis and processing module further includes: The parking space status recognition unit is specifically used for: Based on parking lot images, the YOLO algorithm is used to detect targets in the parking space area. When a vehicle is detected in the parking space area, the vehicle feature information is extracted, including the vehicle outline. By comparing the relative position of the vehicle outline with the parking space boundary line, it is determined whether the vehicle is parked in accordance with regulations; if not, an early warning message is sent to the management terminal. The built-in timestamp function records the parking time of vehicles. If the parking time exceeds the preset duration, it is judged as an abnormal parking space and an early warning message is sent to the management terminal. The OCR recognition algorithm identifies the vehicle's license plate number and compares it with the license plate information in the parking lot's database to determine whether the vehicle is legitimate. If not, it sends an alarm message to the management terminal.

5. The camera-based intelligent parking management system according to claim 4, characterized in that, The target detection in the parking space area using the YOLO algorithm includes: After detecting an object within the parking space area using the YOLO algorithm, the object's outline, size, and texture are matched with a pre-stored vehicle model; if a match is found, the object is preliminarily identified as a suspected vehicle. Multiple frames of parking lot images are continuously acquired, suspected vehicles are identified, and the movement trajectory of the suspected vehicles is tracked. The movement trajectory and the final movement state of the suspected vehicles are used to determine whether they match the characteristics of a vehicle. If they do, the suspected vehicle is identified as a vehicle; otherwise, it is considered a false positive.

6. The camera-based intelligent parking management system according to claim 5, characterized in that, The data storage module is specifically used for: Store parking lot images, number of parking spaces, parking space status, license plate numbers, and corresponding vehicle entry and exit times; Real-time backup of stored information, and data recovery of fields in the event of data anomalies; It interacts with the image analysis and processing module in real time to provide parking space status information.

7. The camera-based intelligent parking management system according to claim 6, characterized in that, The management terminal uses a computer, tablet, or mobile device to read the number of parking spaces, parking space status, license plate number, and corresponding vehicle entry and exit time from the data storage module, and generates parking space usage statistics reports and vehicle entry and exit record reports. The management terminal is equipped with a car owner interface, which supports mobile APP to query real-time parking space information and make reservations.

8. A camera-based intelligent parking space management method, characterized in that, The method employs the camera-based intelligent parking management system as described in any one of claims 1 to 7; The method includes: The camera module captures parking lot images at a preset frequency and transmits them to the image analysis and processing module via the network. The image analysis and processing module uses edge detection and semantic segmentation algorithms to identify parking space boundaries, determine parking space areas, and count the number of parking spaces based on the parking lot image. It also uses object detection and license plate recognition algorithms to determine the status of parking spaces and the legality of parked vehicles. The data storage module stores parking space status information and related vehicle information. The management terminal displays parking space status information in real time and responds to abnormal alarms.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the camera-based intelligent parking space management method as described in claim 8.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the camera-based intelligent parking space management method as described in claim 8.

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