A ship parking ban monitoring method and system based on image processing

Abstract

The present invention discloses a ship parking ban monitoring method based on image processing, comprising: obtaining ship data of a target ship in a port image, wherein the ship data comprises: an amplitude of the brightness intensity of the target ship, a horizontal coordinate of a pixel of the target ship in the port image, a vertical coordinate of a pixel of the target ship in the port image, a horizontal coordinate of a center position of the target ship in the port image, and a vertical coordinate of a center position of the target ship in the port image; setting a ship recognition model, calculating the brightness intensity of the target ship in the port image according to the ship data, determining the shape of the target ship by means of the brightness intensity, and comparing it with the shape of a prohibited ship, so as to judge whether the target ship needs to be prohibited from parking.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship no-stopping monitoring, and more specifically, relates to a ship no-stopping monitoring method and system based on image processing. Background Art

[0002] A ship port ban refers to a regulation that prohibits ships from docking or berthing in a port or designated area. Such regulations are usually formulated by port management agencies, port operators or relevant regulations to ensure the safety, order and efficiency of the port.

[0003] There are many reasons why a ship may be prohibited from stopping at a port:

[0004] 1. Safety considerations: Some areas may have safety hazards, such as insufficient water depth, obstacles, narrow channels or waters, potential submarine obstacles, etc. Prohibiting ships from anchoring in these areas can reduce the potential risk of collision, grounding or other accidents.

[0005] 2. Port management and flow control: Some busy ports need to effectively manage and control the flow of ships. Prohibiting ships from berthing in specific areas can avoid traffic congestion and interference between ships, ensuring the smooth operation of port operations.

[0006] 3. Allocation of ship service facilities: Ports are usually equipped with limited berthing spaces and service facilities, such as docks, berths, power supply and water supply facilities, etc. Prohibiting ships from berthing in certain areas can ensure that these resources are reasonably allocated and utilized, avoiding overcrowding and waste of resources.

[0007] 4. Environmental protection: Some ports may be located in environmentally sensitive areas, such as nature reserves, fish breeding areas or coral reefs. Prohibiting ships from anchoring in these areas can reduce the negative impact on the ecological environment and protect biodiversity and the integrity of the ecosystem.

[0008] The following technical problems exist in reality:

[0009] 1. Complex scenes: Ships have different appearances and shapes, and they may appear in complex environments, such as ports, oceans, or rivers. Observers need to find ships in a large number of images, which is very time-consuming and difficult for large-scale or real-time monitoring tasks.

[0010] 2. High workload: Manual observation requires a large amount of human resources for continuous monitoring, and human fatigue and subjective judgment may lead to missed detection or misjudgment.

[0011] 3. Automation needs: Automated vessel detection can achieve real-time monitoring and alerts, helping to improve efficiency and reduce human errors. Summary of the invention

[0012] In order to solve the above technical problems, the present invention proposes a ship parking ban monitoring method based on image processing, comprising:

[0013] Acquire ship data of a target ship in a port image, wherein the ship data includes: an amplitude of a brightness intensity of the target ship, a horizontal coordinate of a pixel of the target ship in the port image, a vertical coordinate of a pixel of the target ship in the port image, a horizontal coordinate of a center position of the target ship in the port image, and a vertical coordinate of a center position of the target ship in the port image;

[0014] A ship recognition model is set up, and according to the ship data, the brightness intensity of the target ship in the port image is calculated, the shape of the target ship is determined by the brightness intensity, and compared with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking.

[0015] Furthermore, the ship identification model includes:

[0016]

[0017] Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the target ship pixel in the port image, y is the vertical coordinate of the target ship pixel in the port image, f(x, y) is the brightness intensity of the target ship at the coordinate (x, y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image.

[0018] Furthermore, the change rate sigma of the pixel value of the target ship in the port image includes:

[0019] sigma=k*max(|Gx|,|Gy|)

[0020] Wherein, k is a scaling factor, Gx is a horizontal gradient value of the target ship in the port image, and Gy is a vertical gradient value of the target ship in the port image.

[0021] Furthermore, it includes: calculating the horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image by using a Sobel operator.

[0022] Furthermore, it includes: the larger the value of the change rate sigma of the pixel value of the target ship in the port image, the smoother the brightness distribution of the target ship in the port image; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image, the sharper the brightness distribution of the target ship.

[0023] The present invention also proposes a ship parking ban monitoring system based on image processing, comprising:

[0024] A data acquisition module is used to acquire ship data of a target ship in a port image, wherein the ship data includes: the amplitude of the brightness intensity of the target ship, the horizontal coordinate of the pixel of the target ship in the port image, the vertical coordinate of the pixel of the target ship in the port image, the horizontal coordinate of the center position of the target ship in the port image, and the vertical coordinate of the center position of the target ship in the port image;

[0025] The recognition module is used to set a ship recognition model, calculate the brightness intensity of the target ship in the port image according to the ship data, determine the shape of the target ship by the brightness intensity, and compare it with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking.

[0026] Furthermore, the ship identification model includes:

[0027]

[0028] Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the target ship pixel in the port image, y is the vertical coordinate of the target ship pixel in the port image, f(x,y) is the brightness intensity of the target ship at the coordinate (x,y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image.

[0029] Furthermore, the change rate sigma of the pixel value of the target ship in the port image includes:

[0030] sigma = k*max(|Gx|,|Gy|)

[0031] Wherein, k is a scaling factor, Gx is a horizontal gradient value of the target ship in the port image, and Gy is a vertical gradient value of the target ship in the port image.

[0032] Furthermore, it includes: calculating the horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image by using a Sobel operator.

[0033] Furthermore, it includes: the larger the value of the change rate sigma of the pixel value of the target ship in the port image, the smoother the brightness distribution of the target ship in the port image; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image, the sharper the brightness distribution of the target ship.

[0034] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0035] The present invention obtains the ship data of the target ship in the port image, wherein the ship data includes: the amplitude of the brightness intensity of the target ship, the horizontal coordinate of the target ship pixel in the port image, the vertical coordinate of the target ship pixel in the port image, the horizontal coordinate of the center position of the target ship in the port image, and the vertical coordinate of the center position of the target ship in the port image; a ship recognition model is set, and the brightness intensity of the target ship in the port image is calculated according to the ship data, and the shape of the target ship is determined by the brightness intensity, and compared with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking. The present invention can automatically identify the type of ship that needs to be prohibited from parking in the port through the above technical solution, improve the safety of ports and ships, and greatly improve the monitoring efficiency and liberate human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0037] Figure 2 It is a structural diagram of the system of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0038] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0039] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0040] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0041] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0042] The display screen is used to display the user interface of each application.

[0043] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0044] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0045] Example 1

[0046] like Figure 1 As shown, an embodiment of the present invention provides a ship parking ban monitoring method based on image processing, comprising:

[0047] Step 101, acquiring ship data of a target ship in a port image, wherein the ship data includes: an amplitude of brightness intensity of the target ship, a horizontal coordinate of a pixel of the target ship in the port image, a vertical coordinate of a pixel of the target ship in the port image, a horizontal coordinate of a center position of the target ship in the port image, and a vertical coordinate of a center position of the target ship in the port image;

[0048] Step 102, setting a ship recognition model, calculating the brightness intensity of the target ship in the port image according to the ship data, determining the shape of the target ship by the brightness intensity, and comparing it with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking.

[0049] Specifically, the ship identification model includes:

[0050]

[0051] Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the target ship pixel in the port image, y is the vertical coordinate of the target ship pixel in the port image, f(x, y) is the brightness intensity of the target ship at the coordinate (x, y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image.

[0052] Specifically, the change rate sigma of the pixel value of the target ship in the port image includes:

[0053] sigma = k*max(|Gx|,|Gy|)

[0054] Wherein, k is a scaling factor, Gx is a horizontal gradient value of the target ship in the port image, and Gy is a vertical gradient value of the target ship in the port image.

[0055] Specifically, the horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image are calculated by using the Sobel operator.

[0056] Specifically, the larger the value of the change rate sigma of the pixel value of the target ship in the port image is, the smoother the brightness distribution of the target ship in the port image is; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image is, the sharper the brightness distribution of the target ship is.

[0057] Example 2

[0058] like Figure 2 As shown, an embodiment of the present invention further provides a ship parking ban monitoring system based on image processing, comprising:

[0059] A data acquisition module is used to acquire ship data of a target ship in a port image, wherein the ship data includes: the amplitude of the brightness intensity of the target ship, the horizontal coordinate of the pixel of the target ship in the port image, the vertical coordinate of the pixel of the target ship in the port image, the horizontal coordinate of the center position of the target ship in the port image, and the vertical coordinate of the center position of the target ship in the port image;

[0060] The recognition module is used to set a ship recognition model, calculate the brightness intensity of the target ship in the port image according to the ship data, determine the shape of the target ship by the brightness intensity, and compare it with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking.

[0061] Specifically, the ship identification model includes:

[0062]

[0063] Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the target ship pixel in the port image, y is the vertical coordinate of the target ship pixel in the port image, f(x, y) is the brightness intensity of the target ship at the coordinate (x, y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image.

[0064] Specifically, the change rate sigma of the pixel value of the target ship in the port image includes:

[0065] sigma = k*max(|Gx|,|Gy|)

[0066] Wherein, k is a scaling factor, Gx is a horizontal gradient value of the target ship in the port image, and Gy is a vertical gradient value of the target ship in the port image.

[0067] Specifically, the horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image are calculated by using the Sobel operator.

[0068] Specifically, the larger the value of the change rate sigma of the pixel value of the target ship in the port image is, the smoother the brightness distribution of the target ship in the port image is; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image is, the sharper the brightness distribution of the target ship is.

[0069] Example 3

[0070] The embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the image processing-based ship parking ban monitoring method.

[0071] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0072] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining ship data of a target ship in a port image, wherein the ship data includes: an amplitude of a brightness intensity of the target ship, a horizontal coordinate of a pixel of the target ship in the port image, a vertical coordinate of a pixel of the target ship in the port image, a horizontal coordinate of a center position of the target ship in the port image, and a vertical coordinate of a center position of the target ship in the port image;

[0073] Step 102, setting a ship recognition model, calculating the brightness intensity of the target ship in the port image according to the ship data, determining the shape of the target ship by the brightness intensity, and comparing it with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking.

[0074] Specifically, the ship identification model includes:

[0075]

[0076] Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the target ship pixel in the port image, y is the vertical coordinate of the target ship pixel in the port image, f(x, y) is the brightness intensity of the target ship at the coordinate (x, y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image.

[0077] Specifically, the change rate sigma of the pixel value of the target ship in the port image includes:

[0078] sigma=k*max(|Gx|,|Gy|), wherein k is a scaling factor, Gx is a horizontal gradient value of the target ship in the port image, and Gy is a vertical gradient value of the target ship in the port image.

[0079] Specifically, the horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image are calculated by using the Sobel operator.

[0080] Specifically, the larger the value of the change rate sigma of the pixel value of the target ship in the port image is, the smoother the brightness distribution of the target ship in the port image is; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image is, the sharper the brightness distribution of the target ship is.

[0081] Example 4

[0082] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the image processing-based ship no-stop monitoring method.

[0083] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0084] Among them, the storage medium can be used to store software programs and modules, such as a ship parking ban monitoring method based on image processing in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned ship parking ban monitoring method based on image processing. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0085] The processor may call the information and application program stored in the storage medium through the transmission system to execute the following steps: Step 101, obtaining the ship data of the target ship in the port image, wherein the ship data includes: the amplitude of the brightness intensity of the target ship, the horizontal coordinate of the pixel of the target ship in the port image, the vertical coordinate of the pixel of the target ship in the port image, the horizontal coordinate of the center position of the target ship in the port image, and the vertical coordinate of the center position of the target ship in the port image;

[0086] Step 102, setting a ship recognition model, calculating the brightness intensity of the target ship in the port image according to the ship data, determining the shape of the target ship by the brightness intensity, and comparing it with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking.

[0087] Specifically, the ship identification model includes:

[0088]

[0089] Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the target ship pixel in the port image, y is the vertical coordinate of the target ship pixel in the port image, f(x, y) is the brightness intensity of the target ship at the coordinate (x, y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image.

[0090] Specifically, the change rate sigma of the pixel value of the target ship in the port image includes:

[0091] sigma = k*max(|Gx|,|Gy|)

[0092] Wherein, k is a scaling factor, Gx is a horizontal gradient value of the target ship in the port image, and Gy is a vertical gradient value of the target ship in the port image.

[0093] Specifically, the horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image are calculated by using the Sobel operator.

[0094] Specifically, the larger the value of the change rate sigma of the pixel value of the target ship in the port image is, the smoother the brightness distribution of the target ship in the port image is; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image is, the sharper the brightness distribution of the target ship is.

[0095] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0096] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0100] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0101] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A ship parking ban monitoring method based on image processing, characterized in that: include: Acquire ship data of a target ship in a port image, wherein the ship data includes: an amplitude of a brightness intensity of the target ship, a horizontal coordinate of a pixel of the target ship in the port image, a vertical coordinate of a pixel of the target ship in the port image, a horizontal coordinate of a center position of the target ship in the port image, and a vertical coordinate of a center position of the target ship in the port image; Setting a ship recognition model, calculating the brightness intensity of the target ship in the port image according to the ship data, determining the shape of the target ship by the brightness intensity, and comparing it with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking; Specifically, the ship identification model includes: Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the pixel of the target ship in the port image, y is the vertical coordinate of the pixel of the target ship in the port image, f(x, y) is the brightness intensity of the target ship at the coordinate (x, y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image; The change rate sigma of the pixel value of the target ship in the port image includes: sigma = k*max(|Gx|,|Gy|) Wherein, k is the scaling factor, Gx is the horizontal gradient value of the target ship in the port image, and Gy is the vertical gradient value of the target ship in the port image; The horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image are calculated by the Sobel operator.

2. A ship parking ban monitoring method based on image processing as claimed in claim 1, characterized in that: include: The larger the value of the change rate sigma of the pixel value of the target ship in the port image, the smoother the brightness distribution of the target ship in the port image; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image, the sharper the brightness distribution of the target ship.

3. A ship parking ban monitoring system based on image processing, characterized in that: include: A data acquisition module is used to acquire ship data of a target ship in a port image, wherein the ship data includes: the amplitude of the brightness intensity of the target ship, the horizontal coordinate of the pixel of the target ship in the port image, the vertical coordinate of the pixel of the target ship in the port image, the horizontal coordinate of the center position of the target ship in the port image, and the vertical coordinate of the center position of the target ship in the port image; an identification module, used to set a ship identification model, calculate the brightness intensity of the target ship in the port image according to the ship data, determine the shape of the target ship by the brightness intensity, and compare it with the shape of the prohibited ship, so as to determine whether the target ship needs to be prohibited from parking; Specifically, the ship identification model includes: Wherein, A is the amplitude of the brightness intensity of the target ship, x is the horizontal coordinate of the pixel of the target ship in the port image, y is the vertical coordinate of the pixel of the target ship in the port image, f(x, y) is the brightness intensity of the target ship at the coordinate (x, y) in the port image, x0 is the horizontal coordinate of the center position of the target ship in the port image, y0 is the vertical coordinate of the center position of the target ship in the port image, and sigma is the rate of change of the pixel value of the target ship in the port image; The change rate sigma of the pixel value of the target ship in the port image includes: sigma = k*max(|Gx|,|Gy|) Wherein, k is the scaling factor, Gx is the horizontal gradient value of the target ship in the port image, and Gy is the vertical gradient value of the target ship in the port image; The horizontal gradient value Gx of the target ship in the port image and the vertical gradient value Gy of the target ship in the port image are calculated by the Sobel operator.

4. A ship parking ban monitoring system based on image processing as claimed in claim 3, characterized in that: include: The larger the value of the change rate sigma of the pixel value of the target ship in the port image, the smoother the brightness distribution of the target ship in the port image; the smaller the value of the change rate sigma of the pixel value of the target ship in the port image, the sharper the brightness distribution of the target ship.

Images