CN113158844B - Ship supervision method and device and electronic equipment - Google Patents
Ship supervision method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the present specification provides a ship supervision method, which includes the steps of constructing and training a ship recognition model, obtaining image information to be recognized, extracting ship form information from the image information to be recognized by using the ship recognition model, calculating load state data of a ship by using the ship form information, and supervising the ship based on the load state data of the ship, and includes: a risky ship is identified. The load state data of the ship is further calculated by extracting the form information of the ship, automatic risk identification is carried out from the dimension of the load state, the management and control effect is improved, and the management and control risk is reduced.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method and an apparatus for monitoring a ship, and an electronic device.
Background
At present, the automatic supervision of the ship in the industry actually identifies the ship. The risk control level of the mode is low, and the practical application requirements are difficult to meet.
Therefore, it is necessary to provide a method for further improving the management and control effect and reducing the management and control risk.
Disclosure of Invention
The embodiment of the specification provides a ship supervision method, a ship supervision device and electronic equipment, which are used for improving management and control effects and reducing management and control risks.
An embodiment of the present specification provides a ship supervision method, including:
constructing and training a ship recognition model, acquiring image information to be recognized, and extracting ship form information from the image information to be recognized by using the ship recognition model;
calculating load state data of the ship by using the form information of the ship;
supervising a vessel based on the load status data of the vessel, comprising: a risky ship is identified.
Optionally, the building and training a ship recognition model includes:
acquiring a sample image, and marking a target point of a ship in the sample image, wherein the target point comprises a draught point and a ship frame point;
constructing and training a ship recognition model by using the labeled sample image;
the extracting of the shape information of the ship from the image information to be identified by using the ship identification model comprises the following steps:
and extracting the draught point and the ship frame point of the ship in the image to be identified.
Optionally, the calculating the load status data of the ship by using the form information of the ship includes:
and calculating the size of the ship by utilizing the ship frame points, and determining the load state data of the ship by combining the draught points.
Optionally, the calculating the ship size by using the ship frame points comprises:
and (4) performing photographic coordinate conversion by using the coordinates of the ship frame points in the image, and calculating the size of the ship.
Optionally, the method further comprises:
and constructing a coverage model, and identifying the coverage state of the image to be identified by using the coverage model.
Optionally, the identifying a risky vessel further comprises:
and judging the density attribute information of the loaded articles by using the coverage state and the load state data, and carrying out risk identification by combining the density attribute information of the reference articles.
Optionally, the coverage status comprises: uncovered, fully covered and not fully covered.
An embodiment of this specification further provides a ship supervision device, including:
the model module is used for constructing and training a ship recognition model, acquiring image information to be recognized, and extracting the form information of a ship from the image information to be recognized by using the ship recognition model;
the load module is used for calculating load state data of the ship by utilizing the form information of the ship;
an identification supervision module that supervises a vessel based on load status data of the vessel, comprising: a risky ship is identified.
Optionally, the building and training a ship recognition model includes:
acquiring a sample image, and marking a target point of a ship in the sample image, wherein the target point comprises a draught point and a ship frame point;
constructing and training a ship recognition model by using the marked sample image;
the extracting of the shape information of the ship from the image information to be identified by using the ship identification model comprises the following steps:
and extracting the draught point and the ship frame point of the ship in the image to be identified.
Optionally, the calculating the load status data of the ship by using the form information of the ship includes:
and calculating the size of the ship by utilizing the ship frame points, and determining the load state data of the ship by combining the draught points.
Optionally, the calculating the size of the ship by using the ship frame points comprises:
and (4) performing shooting coordinate conversion by using the coordinates of the ship frame points in the image, and calculating the size of the ship.
Optionally, the method further comprises:
and constructing a coverage model, and identifying the coverage state of the image to be identified by using the coverage model.
Optionally, the identifying a risky vessel further comprises:
and judging the density attribute information of the loaded articles by using the coverage state and the load state data, and carrying out risk identification by combining the density attribute information of the reference articles.
Optionally, the coverage status comprises: uncovered, fully covered and not fully covered.
An embodiment of the present specification further provides an electronic device, where the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the above methods.
Various technical solutions provided in an embodiment of the present specification acquire image information to be recognized by constructing and training a ship recognition model, extract ship form information from the image information to be recognized by using the ship recognition model, calculate load state data of a ship by using the ship form information, and supervise the ship based on the load state data of the ship, including: a risky ship is identified. The load state data of the ship is further calculated by extracting the form information of the ship, automatic risk identification is carried out from the dimension of the load state, the management and control effect is improved, and the management and control risk is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of a vessel surveillance method provided in an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a ship supervision device provided in an embodiment of the present specification;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept to those skilled in the art. The same reference numerals denote the same or similar elements, components, or portions in the drawings, and thus, a repetitive description thereof will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram of a ship monitoring method provided in an embodiment of the present specification, where the method may include:
s101: and constructing and training a ship recognition model, acquiring image information to be recognized, and extracting the form information of the ship from the image information to be recognized by using the ship recognition model.
In an embodiment of the present specification, the building and training a ship recognition model may include:
acquiring a sample image, and marking a target point of a ship in the sample image, wherein the target point can comprise a draught point and a ship frame point;
and constructing and training a ship recognition model by using the labeled sample image.
In this embodiment, if the building and training a ship recognition model includes:
acquiring a sample image, and marking a target point of a ship in the sample image, wherein the target point can comprise a draught point and a ship frame point;
constructing and training a ship recognition model by using the labeled sample image;
then, the extracting of the morphological information of the ship from the image information to be recognized by using the ship recognition model may include:
and extracting the draught point and the ship frame point of the ship in the image to be identified.
Thus, the morphological information may include the draught point and the vessel frame point.
Through extracting draft point and ship frame point, can calculate the size of ship, the supervision of being convenient for.
When the ship recognition model is trained, the picture can be reduced and converted into an RGB channel three-dimensional matrix, then convolution operation is carried out on the picture, the picture is divided into regions and subjected to regression operation, the difference between a regression result and a marked target point is determined, and whether model parameters are adjusted or not is judged until the difference between the regression result and the marked target point meets the condition.
Of course, the shape information may also include ship contour information, ship cover and ship boundary information.
Therefore, we can also build a contour recognition model and an overlay model. For identifying the contour of the vessel in the image and for obtaining coverage.
S102: and calculating load state data of the ship by using the shape information of the ship.
In an embodiment of the present disclosure, the calculating load status data of the ship using the form information of the ship may include:
and calculating the size of the ship by utilizing the ship frame points, and determining the load state data of the ship by combining the draught points.
In this embodiment, the calculating the ship size by using the ship frame point may include:
and (4) performing shooting coordinate conversion by using the coordinates of the ship frame points in the image, and calculating the size of the ship.
The photographic coordinate conversion is performed in order to convert the coordinates of the ship in the image into the relative position of the ship with respect to the camera in the three-dimensional space, and the size of the ship can be calculated from the relative positions of the points of the ship with respect to the camera.
Specifically, coordinate conversion can be performed according to the draft angle and the yaw angle of the draft point, the ship frame point and the camera, and the distance from the draft point, the ship frame point and the camera is calculated.
S103: supervising a vessel based on load status data of the vessel, comprising: a risky ship is identified.
The method comprises the steps of acquiring image information to be recognized by constructing and training a ship recognition model, extracting ship form information from the image information to be recognized by using the ship recognition model, calculating load state data of a ship by using the ship form information, and supervising the ship based on the ship load state data, and comprises the following steps: a risky ship is identified. The load state data of the ship is further calculated by extracting the form information of the ship, automatic risk identification is carried out from the dimension of the load state, the management and control effect is improved, and the management and control risk is reduced.
In the embodiment of this specification, still include:
and constructing a coverage model, and identifying the coverage state of the image to be identified by using the coverage model.
In an embodiment of the present specification, the identifying a risky ship further includes:
and judging the density attribute information of the loaded articles by using the coverage state and the load state data, and carrying out risk identification by combining the density attribute information of the reference articles.
Wherein the density attribute information of the reference item may be the density of the item declared by the ship user.
The density attribute information may be a weighted density obtained by weighting and summing the densities of various declared articles in combination with the total weight of the declared articles.
In embodiments of the present description, where the number of draught points identified is multiple, then identifying a risk vessel may further comprise:
and simulating and calculating the reference inclination degree of the ship by using the articles and the article distribution positions in the ship user declaration information, calculating the actual inclination degree of the ship according to the inclinations of the plurality of draught points, comparing the reference inclination degree with the actual inclination degree, and identifying the false declaration information.
In an embodiment of the present specification, the coverage status includes: uncovered, fully covered and not fully covered.
Wherein the overlay model may be a Fast convolutional neural network model (Fast-R-CNN).
When the coverage model is trained specifically, the coverage state of the sample image can be marked, the coverage state is used as a label, and the sample image is used as input for training, so that the trained coverage model can automatically recognize the coverage state of the ship in the subsequent identification image.
In practical use, considering that the labeling formats of the sample images may be complex and diverse, in order to improve compatibility, a format conversion rule may be configured to convert multiple labeling formats into labels in the same format.
Fig. 2 is a schematic structural diagram of a ship supervision apparatus provided in an embodiment of the present specification, where the apparatus may include:
the model module 201 is used for constructing and training a ship recognition model, acquiring image information to be recognized, and extracting ship form information from the image information to be recognized by using the ship recognition model;
a load module 202 for calculating load status data of the ship using the form information of the ship;
an identification supervision module 203 for supervising the vessel based on the load status data of the vessel, comprising: a risky ship is identified.
Optionally, the building and training a ship recognition model includes:
acquiring a sample image, and marking a target point of a ship in the sample image, wherein the target point comprises a draught point and a ship frame point;
constructing and training a ship recognition model by using the marked sample image;
the extracting of the shape information of the ship from the image information to be identified by using the ship identification model comprises the following steps:
and extracting the draught point and the ship frame point of the ship in the image to be identified.
Optionally, the calculating the load status data of the ship by using the form information of the ship includes:
and calculating the size of the ship by utilizing the ship frame points, and determining the load state data of the ship by combining the draught points.
Optionally, the calculating the ship size by using the ship frame points comprises:
and (4) performing shooting coordinate conversion by using the coordinates of the ship frame points in the image, and calculating the size of the ship.
Optionally, the method further comprises:
and constructing a coverage model, and identifying the coverage state of the image to be identified by using the coverage model.
Optionally, the identifying a risky vessel further comprises:
and judging the density attribute information of the loaded articles by using the coverage state and the load state data, and carrying out risk identification by combining the density attribute information of the reference articles.
In an embodiment of the present specification, the coverage status includes: uncovered, fully covered and not fully covered.
Wherein the density attribute information of the reference item may be the density of the item declared by the ship user.
The density attribute information may be a weighted density obtained by weighting and summing the densities of various declared items in combination with the total weight of the declared items.
In an embodiment of the present specification, the draft point is identified as a plurality, and then identifying the risk vessels may further include:
and simulating and calculating the reference inclination degree of the ship by utilizing the articles and the article distribution positions in the ship user declaration information, calculating the actual inclination degree of the ship according to the inclinations of the plurality of draught points, comparing the reference inclination degree with the actual inclination degree, and identifying false declaration information.
The device acquires image information to be recognized by constructing and training a ship recognition model, extracts ship form information from the image information to be recognized by using the ship recognition model, calculates load state data of a ship by using the ship form information, supervises the ship based on the load state data of the ship, and comprises the following steps: a risky ship is identified. The load state data of the ship is further calculated by extracting the form information of the ship, automatic risk identification is carried out from the dimension of the load state, the management and control effect is improved, and the management and control risk is reduced.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting the various system components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.
Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM) 3201 and/or a cache storage unit 3202, and may further include a read only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, to name a few.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
Fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer 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 electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.
While the foregoing detailed description has described in detail certain embodiments of the invention with reference to certain specific aspects, embodiments and advantages thereof, it should be understood that the invention is not limited to any particular computer, virtual machine, or electronic device, as various general purpose machines may implement the invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (5)
1. A method of vessel surveillance, comprising:
constructing and training a ship recognition model, comprising:
acquiring a sample image, and marking a target point of a ship in the sample image, wherein the target point comprises a draught point and a ship frame point;
constructing and training a ship recognition model by using the labeled sample image;
acquiring image information to be recognized, and extracting form information of a ship from the image information to be recognized by using the ship recognition model, wherein the method comprises the following steps: extracting the draught point and the ship frame point of the ship in the image to be identified;
calculating load status data of the vessel using the form information of the vessel, including: calculating the size of the ship by utilizing the ship frame points, and determining the load state data of the ship by combining the draught points;
supervising a ship based on the load status data of the ship, identifying a risk ship, comprising: the ship is characterized in that the number of draught points is multiple, the reference inclination degree of the ship is calculated in a simulation mode by utilizing articles and article distribution positions in ship user declaration information, the actual inclination degree of the ship is calculated according to the inclination degrees of the draught points, the reference inclination degree is compared with the actual inclination degree, and false declaration information is identified.
2. The method of claim 1, wherein said calculating the ship dimension using ship frame points comprises:
and (4) performing shooting coordinate conversion by using the coordinates of the ship frame points in the image, and calculating the size of the ship.
3. A watercraft supervision device, comprising:
a model module to construct and train a vessel recognition model, comprising:
acquiring a sample image, and marking a target point of a ship in the sample image, wherein the target point comprises a draught point and a ship frame point;
constructing and training a ship recognition model by using the marked sample image;
acquiring image information to be identified, and extracting form information of a ship from the image information to be identified by using the ship identification model, wherein the method comprises the following steps: extracting the draught point and the ship frame point of the ship in the image to be identified;
a load module for calculating load status data of the ship using the form information of the ship, comprising: calculating the size of the ship by utilizing the ship frame points, and determining the load state data of the ship by combining the draught points;
the identification supervision module supervises the ship based on the load state data of the ship, identifies a risk ship, and comprises: the ship is characterized in that the number of draught points is multiple, the reference inclination degree of the ship is calculated in a simulation mode by utilizing articles and article distribution positions in ship user declaration information, the actual inclination degree of the ship is calculated according to the inclination degrees of the draught points, the reference inclination degree is compared with the actual inclination degree, and false declaration information is identified.
4. An electronic device, wherein the electronic device comprises:
a processor; and a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-2.
5. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-2.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101145200A (en) * | 2007-10-26 | 2008-03-19 | 浙江工业大学 | Inner river ship automatic identification system of multiple vision sensor information fusion |
| CN101323355A (en) * | 2008-07-24 | 2008-12-17 | 北京中星微电子有限公司 | Ship overload detection system and method |
| CN207968546U (en) * | 2018-01-29 | 2018-10-12 | 陕西省高速公路建设集团公司 | A kind of highway green channel collecting vehicle information Transmission system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008106082A1 (en) * | 2007-02-26 | 2008-09-04 | Thornycroft, Giles & Co., Inc. | System for rapid, secure transport of cargo by sea, and monohull fast ship and arrangement and method for loading and unloading cargo on a ship |
| CN110334680B (en) * | 2019-07-12 | 2021-07-20 | 南京海豚梦智能科技有限公司 | Ship water gauge identification method, system and device based on wall-climbing robot |
| CN111652213A (en) * | 2020-05-24 | 2020-09-11 | 浙江理工大学 | Ship water gauge reading identification method based on deep learning |
-
2021
- 2021-04-02 CN CN202110363803.3A patent/CN113158844B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101145200A (en) * | 2007-10-26 | 2008-03-19 | 浙江工业大学 | Inner river ship automatic identification system of multiple vision sensor information fusion |
| CN101323355A (en) * | 2008-07-24 | 2008-12-17 | 北京中星微电子有限公司 | Ship overload detection system and method |
| CN207968546U (en) * | 2018-01-29 | 2018-10-12 | 陕西省高速公路建设集团公司 | A kind of highway green channel collecting vehicle information Transmission system |
Non-Patent Citations (1)
| Title |
|---|
| 基于计算机视觉的船只吨位估算;朱飞翔;《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》;20120715;全文 * |
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