CN113920447A - Ship harbor detection method and device, computer equipment and storage medium - Google Patents

Ship harbor detection method and device, computer equipment and storage medium Download PDF

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CN113920447A
CN113920447A CN202111206106.3A CN202111206106A CN113920447A CN 113920447 A CN113920447 A CN 113920447A CN 202111206106 A CN202111206106 A CN 202111206106A CN 113920447 A CN113920447 A CN 113920447A
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夏春秋
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Shenzhen Vision Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
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    • G—PHYSICS
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
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    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The embodiment of the application belongs to the field of detection, and relates to a ship harboring detection method, which comprises the following steps: acquiring AIS information through an automatic identification system AIS of the ship; detecting radar information of a ship through a radar; calculating the target position according to the AIS information or the radar information; commanding the camera to a target position, and acquiring a ship picture through the camera; inputting the ship picture into a trained neural network model to obtain ship information; and if the ship information does not accord with the preset rule, sending an alarm. The application also provides a ship entry detection device, computer equipment and a storage medium. The ship entering inspection can be completed.

Description

Ship harbor detection method and device, computer equipment and storage medium
Technical Field
The present disclosure relates to the field of detection, and in particular, to a method and an apparatus for detecting ship entry, a computer device and a storage medium.
Background
With the development of computer vision technology and image parallel processing technology, deep learning has increasingly wide application in military field and civil fields such as aerospace, scientific exploration, astronomical observation, video monitoring and the like. The world famous high-resolution satellite imaging system reaches the sub-meter level and even the high-resolution level of 0.1m, the Jilin I light high-resolution remote sensing satellite optical imaging system can acquire 15 ten thousand square kilometers of high-resolution remote sensing image data every day, and the satellite-borne high-capacity full-color imaging system of the WorldView commercial satellite system of Digitalglobe company can shoot 0.5 m resolution images of up to 50 ten thousand square kilometers every day. Remote sensing image data accumulated by a satellite platform and an unmanned aerial vehicle platform are accumulated continuously, and a lightweight deep learning model which is suitable for a mobile platform, occupies less resources and has high calculation efficiency is urgently needed for target detection and identification tasks of the satellite-borne or airborne platform. However, as the navigation path is more and more complex, the number of ships is more and more, and the photos of the ships cannot be taken for detection.
Disclosure of Invention
The embodiment of the application aims to provide a ship entry detection method, a ship entry detection device, computer equipment and a storage medium, and detection of ship entry is completed.
In order to solve the above technical problem, an embodiment of the present application provides a ship entry detection method, which adopts the following technical scheme:
acquiring AIS information through an automatic identification system AIS of the ship;
detecting radar information of the ship through a radar;
calculating a target position according to the AIS information or the radar information;
commanding a camera to the target position, and acquiring a ship picture through the camera;
inputting the ship picture into a trained neural network model to obtain ship information;
and if the ship information does not accord with the preset rule, sending an alarm.
Further, the radar information includes at least: radar navigational speed information and radar position information, the AIS information comprising at least: the step of calculating the target position according to the AIS information or the radar information specifically includes:
extracting radar speed information, radar position information, AIS position information and AIS speed information from the AIS information and the radar information;
calculating a horizontal azimuth angle, a pitch angle and a focal length value;
and calculating the rotation angle of the camera according to the horizontal azimuth angle, the pitch angle and the focal length value.
Further, if the AIS information is empty, after the step of detecting the ship by the radar to obtain the radar information, the method further includes:
calculating a difference value between the AIS position information and the radar position information to obtain a position difference value;
acquiring a preset coordinate threshold value and a preset navigational speed threshold value;
if the position difference is smaller than or equal to the preset coordinate threshold, calculating the difference between the AIS navigational speed information and the AIS navigational speed information to obtain a navigational speed information difference;
and if the navigational speed information difference is smaller than or equal to the preset navigational speed threshold, fusing the radar information and the AIS information.
Further, if the difference value of the speed information is less than or equal to the preset speed threshold, the step of fusing the radar information and the AIS information specifically includes:
if the navigation speed information difference value is smaller than or equal to the preset navigation speed threshold value, acquiring a preset position weighting coefficient and a preset navigation speed weighting coefficient;
calculating position mean value information according to the preset position weighting coefficient, the radar position information and the AIS position information;
calculating the mean value information of the navigational speed according to the preset navigational speed weighting coefficient, the AIS navigational speed information and the AIS navigational speed information;
merging the position mean value information and the navigation speed mean value information to obtain fused information;
the calculating a target position according to the AIS information or the radar information further includes:
and calculating the target position according to the fusion information.
Further, after the step of detecting the radar information of the ship by a radar if the AIS information is empty, the method further includes:
correlating the radar information and the AIS information to obtain correlated information;
acquiring preset grid parameters;
and calibrating the radar information according to the preset grid parameters and the associated information.
Further, after the step of sending an alarm if the ship information does not conform to the preset rule, the method further includes:
acquiring a plurality of training data and a label corresponding to the training data;
inputting the training data and the corresponding label to the initial convolutional neural network model;
passing the initial convolutional neural network model through
Figure BDA0003305550320000031
Training to obtain a trained convolutional neural network model,
Figure BDA0003305550320000032
representing the weight value obtained by training the kth neuron in the nth layer of the multi-layer perceptron of the trained convolutional neural network model according to the output of the (n-1) th layer of the multi-layer perceptron of the trained convolutional neural network model,
Figure BDA0003305550320000033
to represent
Figure BDA0003305550320000034
Corresponding offset, fi nRepresenting the output of the n layer of the trained convolutional neural network model after the ith training data is input into the trained convolutional neural network model, wherein i is any positive integer, n is a natural number, and when n is the output of the n layer of the trained convolutional neural network modelAt the last layer of the target neural network model, fi nIs the output of the trained convolutional neural network model, fi n-1Representing the output of the (i) th training data at the (n-1) th layer of the target neural network model after the (i) th training data is input into the trained convolutional neural network model;
and deploying the trained convolutional neural network model.
Further, after the step of sending an alarm if the ship information does not conform to the preset rule, the method further includes:
fusing the radar information and the AIS information to obtain fused data;
drawing a track graph and a speed change trend graph according to the fused data;
and inputting the flight path graph and the speed change trend graph into the trained convolutional neural network to obtain a predicted flight path graph.
In order to solve the above technical problem, an embodiment of the present application further provides a ship entry detection device, which adopts the following technical scheme:
the automatic identification system comprises an AIS information acquisition module, a ship automatic identification system AIS and a data processing module, wherein the AIS information acquisition module is used for acquiring AIS information through the automatic identification system AIS of the ship;
the radar information acquisition module is used for detecting radar information of the ship through a radar;
the target position calculation module is used for calculating a target position according to the AIS information or the radar information;
the ship picture acquisition module is used for commanding the camera to the target position and acquiring a ship picture through the camera;
the ship information acquisition module is used for inputting the ship picture into the trained neural network model to obtain ship information;
and the alarm module is used for sending out an alarm if the ship information does not accord with the preset rule.
Further, the target position calculation module is further configured to:
extracting radar speed information, radar position information, AIS position information and AIS speed information from the AIS information and the radar information;
calculating a horizontal azimuth angle, a pitch angle of a camera and a focal length value;
and calculating the rotation angle of the camera according to the horizontal azimuth angle, the pitch angle and the focal length value.
Further, the detection device for ship entering a port further comprises a fusion module, wherein the fusion module:
calculating a difference value between the AIS position information and the radar position information to obtain a position difference value;
acquiring a preset coordinate threshold value and a preset navigational speed threshold value;
if the position difference is smaller than or equal to the preset coordinate threshold, calculating the difference between the AIS navigational speed information and the AIS navigational speed information to obtain a navigational speed information difference;
and if the navigational speed information difference is smaller than or equal to the preset navigational speed threshold, fusing the radar information and the AIS information.
Further, the fusion module is further configured to:
if the navigation speed information difference value is smaller than or equal to the preset navigation speed threshold value, acquiring a preset position weighting coefficient and a preset navigation speed weighting coefficient;
calculating position mean value information according to the preset position weighting coefficient, the radar position information and the AIS position information;
calculating the mean value information of the navigational speed according to the preset navigational speed weighting coefficient, the AIS navigational speed information and the AIS navigational speed information;
merging the position mean value information and the navigation speed mean value information to obtain fused information;
the calculating a target position according to the AIS information or the radar information further includes:
and calculating the target position according to the fusion information.
Further, the ship harboring detection device further comprises a calibration module, wherein the calibration module:
correlating the radar information and the AIS information to obtain correlated information;
acquiring preset grid parameters;
and calibrating the radar information according to the preset grid parameters and the associated information.
Further, the detection device for ship entering a port further comprises a training module, wherein the training module:
acquiring a plurality of training data and a label corresponding to the training data;
inputting the training data and the corresponding label to the initial convolutional neural network model;
passing the initial convolutional neural network model through
Figure BDA0003305550320000051
Training to obtain a trained convolutional neural network model,
Figure BDA0003305550320000052
representing the weight value obtained by training the kth neuron in the nth layer of the multi-layer perceptron of the trained convolutional neural network model according to the output of the (n-1) th layer of the multi-layer perceptron of the trained convolutional neural network model,
Figure BDA0003305550320000053
to represent
Figure BDA0003305550320000054
Corresponding offset, fi nRepresenting that i is any positive integer and n is a natural number at the output of the n-th layer of the trained convolutional neural network model after the ith training data is input into the trained convolutional neural network model, and f is the last layer of the target neural network modeli nIs the output of the trained convolutional neural network model, fi n-1Representing the ith training data after being input into the trained convolutional neural network model, and the ith training data is input into the target neural network modelThe output of layer n-1;
and deploying the trained convolutional neural network model.
Further, the ship harboring detection device further comprises a prediction module, wherein the prediction module:
fusing the radar information and the AIS information to obtain fused data;
drawing a track graph and a speed change trend graph according to the fused data;
and inputting the flight path graph and the speed change trend graph into the trained convolutional neural network to obtain a predicted flight path graph.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
a computer device comprising at least one processor, a memory and an input/output unit connected, wherein the memory is used for storing computer readable instructions, and the processor is used for calling the computer readable instructions in the memory to execute the steps of the ship harbor detection method.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium having computer readable instructions stored thereon, which when executed by a processor, implement the steps of the ship harbor detection method described above.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
through obtaining AIS information and radar information, judge whether the position of target is accurate according to the precision of AIS information and radar, select the higher data of precision as calculation data, calculate the target position. Then, the angle of the camera needing to rotate is calculated according to the target position and the relative position of the camera, so that the camera can acquire the picture of the ship. And acquiring the information of the ship according to the picture to judge whether the ship meets the condition of entering a port.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a ship entry detection method according to the present application;
FIG. 3 is a schematic structural view of one embodiment of a ship entry detection device according to the present application;
FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the ship entry detection method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the ship entry detection apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow diagram of one embodiment of a method of vessel harbor inspection according to the present application is shown. The ship harboring detection method comprises the following steps:
step 201, AIS information is obtained through an automatic identification system AIS of a ship.
In the present embodiment, an Automatic Identification System (AIS) refers to a new navigation aid System applied to marine safety and communication between ships and shore, and between ships. The system is usually composed of a VHF communicator, a GPS locator and a communication controller connected with a ship-borne display, a sensor and the like, and can automatically exchange important information such as ship position, navigational speed, course, ship name, call sign and the like. The AIS installed on the ship sends the information outwards and simultaneously receives the information of other ships in the VHF coverage range, so that automatic response is realized. In addition, as an open data transmission system, the system can be connected with terminal equipment such as radar, ARPA, ECDIS, VTS and the like and INTERNET to form a maritime traffic management and monitoring network, is an effective means for obtaining traffic information without radar detection, and can effectively reduce ship collision accidents.
And 202, detecting radar information of the ship through a radar.
In this embodiment, the radar information refers to data that is obtained by checking whether a target reflected echo is included or not based on a signal received by the radar, and detecting a target state from the reflected echo. A signal is a carrier of information, which is the content of the signal.
And 203, calculating the target position according to the AIS information or the radar information.
In this embodiment, some vessels are not equipped with AIS equipment, such as warships, cruise ships, fishing boats, and some shore-based platforms, among others. Even a vessel equipped with an AIS may shut down if the captain believes that continued opening of the AIS would compromise the vessel's safety. Particularly, sea areas where maritime affairs are likely to happen are often ports and nearby sea areas, and since many fishing boats are in the sea areas and the navigation channels are complex, AIS is not needed for detection and calculation, and radar matching is needed.
And step 204, commanding a camera to the target position, and acquiring a ship picture through the camera.
In the present embodiment, the camera is generally an optical camera. By acquiring the position of the ship, the pitch angle, the azimuth angle and the focal length of the optical camera are adjusted to capture the ship picture.
And step 205, inputting the ship picture into the trained neural network model to obtain ship information.
In this embodiment, the ship picture is identified through the neural network model, so as to obtain the type of the ship, such as a fishing boat, a commercial boat or a warship, and the size of the ship.
And step 206, if the ship information does not accord with the preset rule, sending an alarm.
In this embodiment, it is determined whether the information of the ship meets the conditions for entry.
In this embodiment, through obtaining AIS information and radar information, judge whether the position of target is accurate according to the precision of AIS information and radar, select the higher data of precision as calculation data, calculate the target location. Then, the angle of the camera needing to rotate is calculated according to the target position and the relative position of the camera, so that the camera can acquire the picture of the ship. And acquiring the information of the ship according to the picture to judge whether the ship meets the condition of entering a port.
In some optional implementations, the radar information includes at least: radar navigational speed information and radar position information, the AIS information comprising at least: the step of calculating the target position according to the AIS information or the radar information specifically includes:
extracting radar speed information, radar position information, AIS position information and AIS speed information from the AIS information and the radar information;
calculating a horizontal azimuth angle, a pitch angle and a focal length value;
and calculating the rotation angle of the camera according to the horizontal azimuth angle, the pitch angle and the focal length value.
In the above embodiment, the horizontal azimuth is set to 0 degree in the true north direction, i.e., in the true north directionThe direction is taken as the initial angle. With the counterclockwise direction being the positive direction. And calculating the horizontal azimuth angle by a Gaussian average argument back calculation algorithm. The pitch angle of the camera parallel to the sea level is set to be 0 degree, the pitch angle of upward elevation is set to be a positive direction, and the pitch angle of downward depression is set to be a negative direction. The pitch angle of the camera can be calculated according to the initial height and the horizontal azimuth angle of the camera. Because the difference of ship size is great, consequently need revise the focus value according to ship size: f. of1＝k1*D+k2L, L is the length of the ship, D is the distance between the ship and the camera, k1As a focusing distance coefficient, k2To focus on the target length coefficient. And calculating to obtain the angle required to rotate by acquiring the difference between the current camera angle and the horizontal azimuth angle and the pitch angle. The calculation of the rotation angle of the camera is completed through the method.
In some optional implementation manners, after the step of detecting, by a radar, the ship to obtain radar information if the AIS information is empty, the method further includes:
calculating a difference value between the AIS position information and the radar position information to obtain a position difference value;
acquiring a preset coordinate threshold value and a preset navigational speed threshold value;
if the position difference is smaller than or equal to the preset coordinate threshold, calculating the difference between the AIS navigational speed information and the AIS navigational speed information to obtain a navigational speed information difference;
and if the navigational speed information difference is smaller than or equal to the preset navigational speed threshold, fusing the radar information and the AIS information.
In the above embodiment, if the information checked by the AIS is almost consistent with the radar information and the error is within the tolerance range, the two data are subjected to data fusion, so that the detection accuracy is further improved.
In some optional implementation manners, if the speed information difference is less than or equal to the preset speed threshold, the step of fusing the radar information and the AIS information specifically includes:
if the navigation speed information difference value is smaller than or equal to the preset navigation speed threshold value, acquiring a preset position weighting coefficient and a preset navigation speed weighting coefficient;
calculating position mean value information according to the preset position weighting coefficient, the radar position information and the AIS position information;
calculating the mean value information of the navigational speed according to the preset navigational speed weighting coefficient, the AIS navigational speed information and the AIS navigational speed information;
and combining the position mean value information and the navigation speed mean value information to obtain fusion information.
The calculating a target position according to the AIS information or the radar information further includes:
and calculating the target position according to the fusion information.
In the above embodiment, different confidence levels are given to the AIS equipment and the radar equipment according to the detection accuracy of the AIS equipment and the radar equipment, and the more accurate the detection accuracy, the higher the confidence level is, and the higher the corresponding proportion is. Therefore, the accuracy of data fusion is improved, and the calculated position is more accurate.
In some optional implementation manners, after the step of detecting the radar information of the ship by using a radar if the AIS information is empty, the method further includes:
correlating the radar information and the AIS information to obtain correlated information;
acquiring preset grid parameters;
and calibrating the radar information according to the preset grid parameters and the associated information.
In the above embodiment, the grid parameters substantially divide the radar area into a plurality of grid values, wherein the radar information and the AIS information have differences, and the differences are to be solved by a correction, and the correction factor includes: a velocity correction factor, a range correction factor, and an azimuth correction factor. The AIS information and the radar information can be calibrated by debugging the correction factors.
In some optional implementation manners, after the step of sending an alarm if the ship information does not meet the preset rule, the method further includes:
acquiring a plurality of training data and a label corresponding to the training data;
inputting the training data and the corresponding label to the initial convolutional neural network model;
passing the initial convolutional neural network model through
Figure BDA0003305550320000121
Training to obtain a trained convolutional neural network model,
Figure BDA0003305550320000122
representing the weight value obtained by training the kth neuron in the nth layer of the multi-layer perceptron of the trained convolutional neural network model according to the output of the (n-1) th layer of the multi-layer perceptron of the trained convolutional neural network model,
Figure BDA0003305550320000123
to represent
Figure BDA0003305550320000124
Corresponding offset, fi nRepresenting that i is any positive integer and n is a natural number at the output of the n-th layer of the trained convolutional neural network model after the ith training data is input into the trained convolutional neural network model, and f is the last layer of the target neural network modeli nIs the output of the trained convolutional neural network model, fi n-1Representing the output of the (i) th training data at the (n-1) th layer of the target neural network model after the (i) th training data is input into the trained convolutional neural network model;
and deploying the trained convolutional neural network model.
In the above embodiment, the training of the convolutional neural network is completed by the above method, so that the convolutional neural network can complete the identification of the related image.
In some optional implementation manners, after the step of sending an alarm if the ship information does not meet the preset rule, the method further includes:
fusing the radar information and the AIS information to obtain fused data;
drawing a track graph and a speed change trend graph according to the fused data;
and inputting the flight path graph and the speed change trend graph into the trained convolutional neural network to obtain a predicted flight path graph.
In the above embodiment, the AIS information and the radar information are time-synchronized and calibrated to generate corresponding point cloud maps, and the point cloud maps are combined to obtain a combined point cloud map. And drawing the ship track by a least square method. And drawing the transmitted flight path, establishing a corresponding prediction model, and predicting the course and the position of the ship at the next moment. After the drawn flight path is obtained through information correlation fusion, a prediction model is used for carrying out correlation analysis on the flight path, and a system with the main body of system analysis, evaluation, modeling, prediction, decision, control and optimization is established.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a ship entry detection apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various electronic devices.
As shown in fig. 3, the ship entry detection device 300 according to the present embodiment includes: the system comprises an AIS information acquisition module 301, a radar information acquisition module 302, a target position calculation module 303, a ship picture acquisition module 304, a ship information acquisition module 305 and an alarm module 306. Wherein:
the AIS information acquisition module 301 is used for acquiring AIS information through an automatic identification system AIS of the ship;
the radar information acquisition module 302 is configured to detect radar information of the ship through a radar;
the target position calculating module 303 is configured to calculate a target position according to the AIS information or the radar information;
the ship picture acquiring module 304 is configured to instruct the camera to the target position, and acquire a ship picture through the camera;
the ship information acquisition module 305 is configured to input the ship picture into the trained neural network model to obtain ship information;
the alarm module 306 is configured to send an alarm if the ship information does not meet a preset rule.
Further, the target position calculation module 303 is further configured to:
extracting radar speed information, radar position information, AIS position information and AIS speed information from the AIS information and the radar information;
calculating a horizontal azimuth angle, a pitch angle of a camera and a focal length value;
and calculating the rotation angle of the camera according to the horizontal azimuth angle, the pitch angle and the focal length value.
Further, the detection device for ship entering a port further comprises a fusion module, wherein the fusion module:
calculating a difference value between the AIS position information and the radar position information to obtain a position difference value;
acquiring a preset coordinate threshold value and a preset navigational speed threshold value;
if the position difference is smaller than or equal to the preset coordinate threshold, calculating the difference between the AIS navigational speed information and the AIS navigational speed information to obtain a navigational speed information difference;
and if the navigational speed information difference is smaller than or equal to the preset navigational speed threshold, fusing the radar information and the AIS information.
Further, the fusion module is further configured to:
if the navigation speed information difference value is smaller than or equal to the preset navigation speed threshold value, acquiring a preset position weighting coefficient and a preset navigation speed weighting coefficient;
calculating position mean value information according to the preset position weighting coefficient, the radar position information and the AIS position information;
calculating the mean value information of the navigational speed according to the preset navigational speed weighting coefficient, the AIS navigational speed information and the AIS navigational speed information;
merging the position mean value information and the navigation speed mean value information to obtain fused information;
the calculating a target position according to the AIS information or the radar information further includes:
and calculating the target position according to the fusion information.
Further, the ship harboring detection device further comprises a calibration module, wherein the calibration module:
correlating the radar information and the AIS information to obtain correlated information;
acquiring preset grid parameters;
and calibrating the radar information according to the preset grid parameters and the associated information.
Further, the detection device for ship entering a port further comprises a training module, wherein the training module:
acquiring a plurality of training data and a label corresponding to the training data;
inputting the training data and the corresponding label to the initial convolutional neural network model;
passing the initial convolutional neural network model through
Figure BDA0003305550320000151
Training to obtain a trained convolutional neural network model,
Figure BDA0003305550320000152
representing the weight value obtained by training the kth neuron in the nth layer of the multi-layer perceptron of the trained convolutional neural network model according to the output of the (n-1) th layer of the multi-layer perceptron of the trained convolutional neural network model,
Figure BDA0003305550320000153
to represent
Figure BDA0003305550320000154
Corresponding offset, fi nRepresenting that i is any positive integer and n is a natural number at the output of the n-th layer of the trained convolutional neural network model after the ith training data is input into the trained convolutional neural network model, and f is the last layer of the target neural network modeli nIs the output of the trained convolutional neural network model, fi n-1Representing the output of the (i) th training data at the (n-1) th layer of the target neural network model after the (i) th training data is input into the trained convolutional neural network model;
and deploying the trained convolutional neural network model.
Further, the ship harboring detection device further comprises a prediction module, wherein the prediction module:
fusing the radar information and the AIS information to obtain fused data;
drawing a track graph and a speed change trend graph according to the fused data;
and inputting the flight path graph and the speed change trend graph into the trained convolutional neural network to obtain a predicted flight path graph.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the computer device 6. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various types of application software, such as computer readable instructions of a ship entry detection method. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the ship harbor detection method.
The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions, which are executable by at least one processor, so as to cause the at least one processor to execute the steps of the ship harbor entry detection method as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims (10)

1. A ship harbor detection method is characterized by comprising the following steps:
acquiring AIS information through an automatic identification system AIS of the ship;
detecting radar information of the ship through a radar;
calculating a target position according to the AIS information or the radar information;
commanding a camera to the target position, and acquiring a ship picture through the camera;
inputting the ship picture into a trained neural network model to obtain ship information;
and if the ship information does not accord with the preset rule, sending an alarm.
2. The ship harbor detection method according to claim 1, wherein the radar information at least includes: radar navigational speed information and radar position information, the AIS information comprising at least: the step of calculating the target position according to the AIS information or the radar information specifically includes:
extracting radar speed information, radar position information, AIS position information and AIS speed information from the AIS information and the radar information;
calculating a horizontal azimuth angle, a pitch angle and a focal length value;
and calculating the rotation angle of the camera according to the horizontal azimuth angle, the pitch angle and the focal length value.
3. The method according to claim 2, wherein after the step of obtaining the radar information by detecting the ship through the radar if the AIS information is empty, the method further comprises:
calculating a difference value between the AIS position information and the radar position information to obtain a position difference value;
acquiring a preset coordinate threshold value and a preset navigational speed threshold value;
if the position difference is smaller than or equal to the preset coordinate threshold, calculating the difference between the AIS navigational speed information and the AIS navigational speed information to obtain a navigational speed information difference;
and if the navigational speed information difference is smaller than or equal to the preset navigational speed threshold, fusing the radar information and the AIS information.
4. The ship harbor detection method according to claim 3, wherein the step of fusing the radar information and the AIS information if the difference between the speed information is less than or equal to the preset speed threshold specifically comprises:
if the navigation speed information difference value is smaller than or equal to the preset navigation speed threshold value, acquiring a preset position weighting coefficient and a preset navigation speed weighting coefficient;
calculating position mean value information according to the preset position weighting coefficient, the radar position information and the AIS position information;
calculating the mean value information of the navigational speed according to the preset navigational speed weighting coefficient, the AIS navigational speed information and the AIS navigational speed information;
merging the position mean value information and the navigation speed mean value information to obtain fused information;
the calculating a target position according to the AIS information or the radar information further includes:
and calculating the target position according to the fusion information.
5. The method according to claim 1, wherein the step of detecting the radar information of the ship by radar if the AIS information is empty further comprises:
correlating the radar information and the AIS information to obtain correlated information;
acquiring preset grid parameters;
and calibrating the radar information according to the preset grid parameters and the associated information.
6. The method for detecting the arrival of the ship at the port as claimed in claim 1, wherein after the step of issuing an alarm if the ship information does not meet the preset rule, the method further comprises:
acquiring a plurality of training data and a label corresponding to the training data;
inputting the training data and the corresponding label to the initial convolutional neural network model;
passing the initial convolutional neural network model through
Figure FDA0003305550310000021
Training to obtain a trained convolutional neural network model,
Figure FDA0003305550310000022
representing the weight value obtained by training the kth neuron in the nth layer of the multi-layer perceptron of the trained convolutional neural network model according to the output of the (n-1) th layer of the multi-layer perceptron of the trained convolutional neural network model,
Figure FDA0003305550310000023
to represent
Figure FDA0003305550310000024
Corresponding offset, fi nRepresenting that i is any positive integer and n is a natural number at the output of the n-th layer of the trained convolutional neural network model after the ith training data is input into the trained convolutional neural network model, and f is the last layer of the target neural network modeli nIs the output of the trained convolutional neural network model, fi n-1Representing the output of the (i) th training data at the (n-1) th layer of the target neural network model after the (i) th training data is input into the trained convolutional neural network model;
and deploying the trained convolutional neural network model.
7. The method for detecting the arrival of a ship at a port as claimed in claim 6, wherein after the step of issuing an alarm if the ship information does not comply with the preset rule, the method further comprises:
fusing the radar information and the AIS information to obtain fused data;
drawing a track graph and a speed change trend graph according to the fused data;
and inputting the flight path graph and the speed change trend graph into the trained convolutional neural network to obtain a predicted flight path graph.
8. A ship harbor detection device is characterized by comprising:
the automatic identification system comprises an AIS information acquisition module, a ship automatic identification system AIS and a data processing module, wherein the AIS information acquisition module is used for acquiring AIS information through the automatic identification system AIS of the ship;
the radar information acquisition module is used for detecting radar information of the ship through a radar;
the target position calculation module is used for calculating a target position according to the AIS information or the radar information;
the ship picture acquisition module is used for commanding the camera to the target position and acquiring a ship picture through the camera;
the ship information acquisition module is used for inputting the ship picture into the trained neural network model to obtain ship information;
and the alarm module is used for sending out an alarm if the ship information does not accord with the preset rule.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of a method of detecting a ship's entry according to any one of claims 1 to 7.
10. A computer-readable storage medium having computer-readable instructions stored thereon which, when executed by a processor, implement the steps of the ship harbor detection method according to any one of claims 1 to 7.
CN202111206106.3A 2021-10-15 2021-10-15 Ship harbor detection method and device, computer equipment and storage medium Pending CN113920447A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114820983A (en) * 2022-04-27 2022-07-29 天津大学 Dynamic irregular grid division method, equipment and medium for navigation safety evaluation
CN115856873A (en) * 2022-11-15 2023-03-28 大连海事大学 Shore-based AIS signal credibility discrimination model, method and device

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114820983A (en) * 2022-04-27 2022-07-29 天津大学 Dynamic irregular grid division method, equipment and medium for navigation safety evaluation
CN114820983B (en) * 2022-04-27 2023-01-17 天津大学 Dynamic irregular grid division method, equipment and medium for navigation safety evaluation
CN115856873A (en) * 2022-11-15 2023-03-28 大连海事大学 Shore-based AIS signal credibility discrimination model, method and device
CN115856873B (en) * 2022-11-15 2023-11-07 大连海事大学 Bank-based AIS signal credibility judging model, method and device

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