CN112633165A - Vehicle compartment-based sampling supervision method and system, storage medium and electronic equipment - Google Patents
Vehicle compartment-based sampling supervision method and system, storage medium and electronic equipment Download PDFInfo
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Abstract
The application belongs to the technical field of data processing, and particularly relates to a vehicle compartment-based sampling supervision method and system, a storage medium and electronic equipment. The method comprises the following steps: the method comprises the steps of firstly obtaining a sampling image corresponding to a sampling area collected in real time, obtaining position information of a vehicle compartment, and then carrying out sampling supervision on the vehicle compartment according to the position information and a preset sampling rule. The sampling supervision work of the vehicle compartment can be automated by matching the sampling image of the sampling area with the pre-configured sampling rule, the traditional manual or semi-manual semi-automatic sampling supervision mode is replaced, and the vehicle compartment sampling is supervised with low working cost and high accuracy.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a vehicle compartment-based sampling supervision method and system, a storage medium and electronic equipment.
Background
The sampling system samples the vehicle compartment, and the existing method for sampling and supervising the sampling work is carried out manually or semi-manually and semi-automatically, namely, whether the sampling of the vehicle compartment is carried out according to the established sampling rule is monitored simply by manual visual observation or manually by the camera system. The traditional sampling supervision method adopting a manual or semi-manual semi-automatic mode consumes a large amount of manpower, has high working cost and lower accuracy of sampling supervision, and can have fraud behaviors.
Content of application
The embodiment of the application provides a vehicle compartment-based sampling and monitoring method, a vehicle compartment-based sampling and monitoring system, a storage medium and electronic equipment, and aims to solve the technical problems of high working cost and low sampling accuracy caused by a traditional manual observation mode sampling and monitoring method.
In a first aspect, an embodiment of the present application provides a vehicle compartment-based sampling supervision method, where the method includes:
acquiring position information of the vehicle compartment according to a sampling image corresponding to a sampling area acquired in real time;
and carrying out sampling supervision on the vehicle compartment according to the position information and a preset sampling rule.
Optionally, the acquiring the position information of the vehicle compartment according to the sampling image corresponding to the sampling area acquired in real time includes:
carrying out binarization processing on the sampling image, and calculating gray information of the sampling image;
extracting a contour region of the vehicle compartment from the sampling image according to the gray information;
and acquiring the position information of the vehicle compartment according to the contour area.
Optionally, the extracting, from the sampled image according to the gray scale information, a contour region of the vehicle compartment includes:
comparing the gray information of the sampled image with a gray threshold value to obtain a comparison result;
and determining a contour area corresponding to the vehicle compartment according to the comparison result.
Optionally, the acquiring the position information of the vehicle compartment according to the contour area specifically includes:
drawing a minimum bounding rectangle based on the outline region;
and acquiring the position information of the vehicle compartment according to the minimum circumscribed rectangle, wherein the position information comprises four vertex coordinates of the minimum circumscribed rectangle.
Optionally, the vehicle compartment is sampled and supervised according to the position information and a preconfigured sampling rule, and the method includes:
equally dividing the minimum circumscribed rectangle into a preset number of sub-sampling regions;
and calling a pre-configured sampling rule, and carrying out sampling supervision on the sub-sampling area according to the sampling rule.
Optionally, before the invoking a preconfigured sampling rule and performing sampling supervision on the sub-sampling region according to the sampling rule, the method further includes:
acquiring coordinates of sampling equipment and coordinates of the sub-sampling area;
correspondingly, the calling a pre-configured sampling rule and performing sampling supervision on the sub-sampling area according to the sampling rule specifically includes:
and carrying out sampling supervision on the sampling of the target sub-sampling area according to the coordinates of the sampling equipment and the coordinates of the sub-sampling area, and the target sub-sampling area or the target sub-sampling area and the sampling sequence defined by the sampling rule.
Optionally, the position information of the vehicle compartment is obtained according to the sampling image corresponding to the sampling area acquired in real time, and the method further includes:
and acquiring a sampling image corresponding to the sampling area according to a preset time interval.
Optionally, the sampling supervision of the target sub-sampling area according to the coordinates of the sampling device and the coordinates of the sub-sampling area, and the target sub-sampling area or the target sub-sampling area and the sampling sequence defined by the sampling rule specifically includes:
judging whether the coordinates of the sampling equipment fall into the coordinate range of the sub-sampling area or not;
if yes, judging that the sample is normal;
if not, the sampling is judged to be abnormal.
In a second aspect, embodiments of the present application provide a vehicle compartment-based sampling supervision system, the system including:
the acquisition module is used for acquiring the position information of the vehicle compartment according to the sampling image corresponding to the sampling area acquired in real time;
and the sampling supervision module is used for sampling and supervising the vehicle compartment according to the position information and a preset sampling rule.
In a third aspect, the present embodiments provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement a vehicle compartment-based sampling supervision method as described above.
A fourth aspect of the present embodiments provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of a vehicle compartment-based sampling supervision method as described above.
A fifth aspect of the embodiments of the present application provides a computer program product, which, when running on a terminal device, causes the terminal device to execute the vehicle compartment-based sampling supervision method provided in the first aspect of the embodiments of the present application.
Compared with the prior art, the implementation mode of the invention has the following beneficial effects: the method comprises the steps of firstly obtaining a sampling image corresponding to a sampling area collected in real time, obtaining position information of a vehicle compartment, and then carrying out sampling supervision on the vehicle compartment according to the position information and a preset sampling rule. By matching the sampling image of the sampling area with the pre-configured sampling rule, the sampling supervision work of the vehicle compartment can be automated, a manual or traditional semi-manual semi-automatic sampling supervision mode is replaced, and the vehicle compartment sampling is supervised with low working cost and high accuracy.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a schematic flow chart of a first implementation process of a vehicle compartment-based sampling supervision method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a second implementation process of a vehicle compartment-based sampling supervision method according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a vehicle compartment-based sampling supervision system provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the order of writing each step in this embodiment does not mean the order of execution, and the order of execution of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of this embodiment.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
In order to explain the technical means described in the present application, the following description will be given by way of specific embodiments.
Referring to fig. 1, which is a flowchart of a first implementation procedure of the method for providing vehicle cabin-based sampling supervision according to the embodiment of the present application, for convenience of description, only the parts related to the embodiment of the present application are shown.
A preferred embodiment of the present application may be that, the vehicle compartment-based sampling supervision method includes:
and S101, acquiring the position information of the vehicle compartment according to the sampling image corresponding to the sampling area acquired in real time.
In this embodiment, the sampling area refers to a sampling area that is defined by a user in advance according to an area to be sampled, the sampling area is used for stopping the vehicle compartment, and a sampling supervision system performs sampling supervision on the vehicle compartment in the sampling area. The sampling supervision system comprises a camera for sampling image acquisition, and the area shot by the camera is the sampling area. The sampling image refers to an image shot by the camera to a sampling area.
It should be noted that the sampling region is arranged in a node region of a parking in a vehicle transport aisle, in which the vehicle cabin is used for carrying and transporting the object to be sampled. The area of the sampling region is larger than the area of the vehicle compartment.
Specifically, the position information refers to information corresponding to a position of the vehicle compartment in the sampling area, and the position information may be stored in a form of coordinates in the sampling supervision system, for example, a rectangular outline formed by coordinates of four vertices of the vehicle compartment.
In some possible embodiments, the obtaining the position information of the vehicle compartment according to the sampled image corresponding to the sampled area collected in real time includes:
s201: carrying out binarization processing on the sampling image, and calculating gray information of the sampling image;
after the sampling supervision system acquires the sampling picture corresponding to the vehicle compartment, binarization processing is carried out on the sampling image, and the gray information of the sampling image is calculated to acquire the final average gray information of the sampling image.
In some embodiments that may be implemented, after the sampling monitoring system obtains the sampling image, the sampling monitoring system sequentially performs binary processing on each small region to obtain gray scale information of each small region, where the small region may be each pixel point of the sampling image or a set of multiple pixel points.
S202: and extracting the contour region of the vehicle compartment from the sampling image according to the gray information.
After the sampling supervision system acquires the gray information of the sampling image, the gray information is processed by a preset rule, so that the contour area of the vehicle compartment is screened in the sampling image, and the position information of the vehicle compartment is determined subsequently.
In some possible embodiments, extracting the contour region of the vehicle compartment from the sample image includes:
comparing the gray information of the sampled image with a gray threshold value to obtain a comparison result;
and determining a contour area corresponding to the vehicle compartment according to the comparison result.
And acquiring the position information of the vehicle compartment according to the contour area.
It should be noted that, if the grayscale threshold corresponding to the vehicle compartment is smaller than the grayscale threshold corresponding to the environment where the vehicle compartment is located, the sampling monitoring system sets a grayscale threshold in advance, so as to compare the grayscale information corresponding to the acquired sampled image with the grayscale threshold.
In some possible embodiments, the extracting the contour region of the vehicle compartment from the sample image according to the gray scale information includes:
comparing the gray information of the sampled image with a gray threshold value to obtain a comparison result;
and determining a contour area corresponding to the vehicle compartment according to the comparison result.
That is, after the sampling monitoring system acquires the gray scale information of the sampled image, the gray scale information is sequentially compared with the gray scale threshold value to acquire a comparison result. The comparison result comprises a first comparison result and a second comparison result, the first comparison result means that the current gray information is smaller than the gray threshold, and the second comparison result means that the current gray information is larger than or equal to the gray threshold.
Specifically, after the sampling supervision system obtains a comparison result corresponding to the gray information, a region in which the gray information in the sampled image is smaller than the gray threshold is screened out according to the comparison result, and then a contour region of the vehicle compartment is extracted according to a predefined screening rule. The screening rules are used to screen other areas that are otherwise susceptible to misidentification.
Specifically, after the sampling supervision system extracts the contour region, the vehicle compartment information is extracted and calculated according to the contour region, so as to obtain the position information of the vehicle compartment. The position information of the vehicle compartment can be in various forms of representation, such as coordinates, areas and the like.
In some possible embodiments, the obtaining the position information of the vehicle compartment according to the contour region specifically includes:
drawing a minimum bounding rectangle based on the outline region;
and acquiring the position information of the vehicle compartment according to the minimum circumscribed rectangle, wherein the position information comprises four vertex coordinates of the minimum circumscribed rectangle.
In this embodiment, after the sampling monitoring system acquires the contour region of the vehicle compartment, the sampling monitoring system acquires the shape of the contour region according to the contour region, and automatically draws a minimum circumscribed rectangle according to the shape of the contour region. The minimum bounding rectangle is a rectangle having the smallest area that can include the shape of the outline region.
After the sampling supervision system obtains the minimum external rectangle, the four vertex coordinates are obtained according to the four vertex positions of the minimum external rectangle, and the four vertex coordinates are the position information of the vehicle compartment.
S102: and carrying out sampling supervision on the vehicle compartment according to the position information and a preset sampling rule.
In this implementation, after the sampling monitoring system obtains the position information of the vehicle compartment, a preconfigured sampling rule is called to perform sampling monitoring on sampling work performed in the vehicle compartment. The sampling rule refers to that a sampling device samples goods in the vehicle compartment according to a certain rule, for example, the sampling rule (sampling scheme) is to collect a sampling region corresponding to the leftmost end of the vehicle compartment and then collect a sampling region corresponding to the rightmost end of the vehicle compartment. And then, sampling and monitoring the vehicle compartment, namely, monitoring the sampling process of the goods in the vehicle compartment by the sampling device (such as monitoring the motion track of the sampling device) by a sampling monitoring system, and judging whether the sampling process is performed according to a sampling rule or not.
In some possible embodiments, according to the preferred embodiment of the above steps, the vehicle compartment is subjected to sampling supervision according to the position information and a preconfigured sampling rule, and the method includes:
equally dividing the minimum circumscribed rectangle into a preset number of sub-sampling regions;
and calling a pre-configured sampling rule, and carrying out sampling supervision on the sub-sampling area according to the sampling rule.
After the sampling supervision system obtains the minimum circumscribed rectangle, dividing the minimum circumscribed rectangle into a preset number of equal parts according to a sampling standard (such as a national standard) so as to obtain a preset number of sub-sampling areas; and numbering a preset number of the sub-sampling regions according to a sampling standard. Sampling supervision is carried out according to the number or the serial number of a target sub-sampling area and the sampling sequence (namely the number or the serial number and the sequence of the sampling area selected according to the sampling rule) defined by the sampling rule, for example, the number of the sub-sampling area is 1-18, and any number (such as 3, 9 and 15) of the sub-sampling areas are selected according to the sampling rule for sampling; the sampling supervision is to determine whether the sampling device samples in three sub-sampling areas selected by the sampling rule.
It should be noted that the sub-sampling region is provided for the purpose of accurately sampling the cargo in the vehicle compartment.
In some possible embodiments, before the invoking a preconfigured sampling rule and performing sampling supervision on the sub-sampling region according to the sampling rule, the method further includes:
acquiring coordinates of sampling equipment and coordinates of the sub-sampling area;
correspondingly, the calling a pre-configured sampling rule and performing sampling supervision on the sub-sampling area according to the sampling rule specifically includes:
and carrying out sampling supervision on the sampling of the target sub-sampling area according to the coordinates of the sampling equipment and the coordinates of the sub-sampling area, and the target sub-sampling area and the sampling sequence defined by the sampling rule.
The sampling supervision system determines the coordinates of the sampling device and the coordinates of the sub-sampling area before calling the pre-configured sampling rule, so that the sampling supervision system can perform sampling supervision on the sampling process of the sampling device according to the sampling rule, namely, whether the motion track of the sampling device falls into the coordinate range of the sub-sampling area is determined. Because the motion route of the sampling device converted according to the sampling rule is determined to fall within the coordinate range of the selected sub-sampling area, if the motion track of the sampling device observed by the supervision process falls within the coordinate range of the sub-sampling area, the sampling is normal; and vice versa.
In some possibly implemented embodiments, the monitoring sampling the target sub-sampling region according to the coordinates of the sampling device and the coordinates of the sub-sampling region, and the target sub-sampling region and the sampling order defined by the sampling rule specifically includes:
judging whether the coordinates of the sampling equipment fall into the coordinate range of the sub-sampling area or not;
if yes, judging that the sample is normal;
if not, the sampling is judged to be abnormal.
In some other possible embodiments, the position information of the vehicle compartment is obtained according to the sampled image corresponding to the sampled area acquired in real time, and the method further includes:
and acquiring a sampling image corresponding to the sampling area according to a preset time interval.
In this embodiment, in order to avoid resource waste caused by the sampling monitoring system directly shooting the sampling area, the sampling monitoring system may define a time interval, and acquire the sampling image of the sampling area according to the time interval.
In other possible embodiments, the method for the sampling supervision system to determine whether the sub-sampling region sampled by the sampling device conforms to the pre-configured sampling rule may specifically be:
acquiring a working image of the sampling device, wherein the working image refers to an image of the sampling device entering the vehicle compartment;
acquiring target position information of the sampling equipment according to the working image;
if the target position information falls into the sub-sampling area, judging that the sampling is normal;
and if the target position information does not fall into the sub-sampling area, judging that the sampling is abnormal.
In other possible embodiments, the determining whether the coordinates of the sampling device fall within the coordinate range of the sub-sampling region specifically includes:
acquiring a working image of the sampling device, wherein the working image refers to an image of the sampling device entering the vehicle compartment;
extracting vertex coordinates of the sampling equipment and acquiring central coordinates of the target self-sampling area;
calculating the deviation distance from the vertex coordinate to the center coordinate;
comparing the deviation distance with a preset threshold distance;
if the deviation distance is smaller than the preset threshold value distance, the sampling is judged to be normal
And if the deviation distance is larger than the preset threshold distance, judging that the sampling is abnormal.
In the working process of real-world operation, the sampling device may be a sampling drill bit, the vehicle compartment may be a coal car compartment, and the sampling supervision method based on the vehicle compartment may specifically be implemented as follows:
sampling images of the coal car after the coal car is stopped stably in the sampling area are background patterns of the sampling drill bit, the sampling monitoring system divides the carriage of the coal car according to 6 parts in the longitudinal direction and 3 parts in the transverse direction (namely, 18 parts in the sub-sampling area), and sampling numbers are respectively marked according to sampling standards (namely, the sub-sampling area is numbered). The sampling monitoring system acquires position information of the coal car carriage and the sampling device (namely coordinates of the car carriage and the sampling device in a sampling area), and performs sampling monitoring according to a preset sampling rule, wherein the specific sampling monitoring is to acquire the instantaneous sampling coordinate of the sampling drill bit by acquiring an instantaneous sampling image of the sampling drill bit in the coal car carriage, and judge whether the sampling is normal or not by judging whether the instantaneous sampling coordinate of the sampling drill bit falls into a preset sub-sampling area or not. And specifically, when the sampling work is carried out, the dynamic contour with the largest area in the carriage area of the coal car is searched, and the motion track of the drill bit in the image is approximately a straight line, so that the contour of the drill bit is subjected to ellipse fitting, and the vertex coordinate of the long axis of the ellipse is the instantaneous coordinate of sampling of the sampling drill bit.
In other cases, the actual distance between the instantaneous coordinate of the sampling drill and the carriage origin position can be calculated at the same time, specifically, the offset distance between the instantaneous coordinate of the sampling drill and the carriage origin position coordinate can be calculated firstly, then the offset distance is multiplied by the ratio of the actual length or width of the carriage to the carriage length or width image pixel quantity, and the distance value is reported to the fuel control system.
Compared with the prior art, the implementation mode of the invention has the following beneficial effects: the method comprises the steps of firstly obtaining a sampling image corresponding to a sampling area collected in real time, obtaining position information of a vehicle compartment, and then carrying out sampling supervision on the vehicle compartment according to the position information and a preset sampling rule. Through the matching use of the sampling image of the sampling area and the pre-configured sampling rule, the sampling supervision work of the vehicle compartment can be automated, the traditional semi-manual semi-automatic sampling supervision mode is replaced, and the vehicle compartment is sampled and supervised with low working cost and high sampling accuracy.
Corresponding to the vehicle compartment-based sampling supervision method in the foregoing vehicle compartment-based sampling supervision method embodiment, fig. 3 shows a structural block diagram of a vehicle compartment-based sampling system provided in an embodiment of the present application, and for convenience of explanation, only the parts related to the embodiment of the present application are shown.
Referring to fig. 3, a vehicle compartment-based sampling supervision system 300, the system comprising:
the acquisition module 301 is configured to acquire position information of the vehicle compartment according to a sampling image corresponding to a sampling area acquired in real time;
and the sampling supervision module 302 is configured to perform sampling supervision on the vehicle compartment according to the position information and a preconfigured sampling rule.
Optionally, the obtaining module 301 specifically includes:
the calculating unit is used for carrying out binarization processing on the sampling image and calculating the gray information of the sampling image;
an extraction unit configured to extract a contour region of the vehicle compartment from the sample image based on the gradation information;
and the acquisition unit is used for acquiring the position information of the vehicle compartment according to the contour area.
Optionally, the extracting unit is specifically configured to:
comparing the gray information of the sampled image with a gray threshold value to obtain a comparison result;
and determining a contour area corresponding to the vehicle compartment according to the comparison result.
Optionally, the obtaining unit is specifically configured to:
drawing a minimum bounding rectangle based on the outline region;
and acquiring the position information of the vehicle compartment according to the minimum circumscribed rectangle, wherein the position information comprises four vertex coordinates of the minimum circumscribed rectangle.
Optionally, the sampling supervision module 302 includes:
equally dividing the minimum circumscribed rectangle into a preset number of sub-sampling regions;
and calling a pre-configured sampling rule, and carrying out sampling supervision on the sub-sampling area according to the sampling rule.
Optionally, before the invoking a preconfigured sampling rule and performing sampling supervision on the sub-sampling region according to the sampling rule, the method further includes:
acquiring coordinates of sampling equipment and coordinates of the sub-sampling area;
correspondingly, the calling a pre-configured sampling rule and performing sampling supervision on the sub-sampling area according to the sampling rule specifically includes:
and carrying out sampling supervision on the sampling of the target sub-sampling area according to the coordinates of the sampling equipment and the coordinates of the sub-sampling area, and the target sub-sampling area and the sampling sequence defined by the sampling rule.
Optionally, the system 300 further comprises:
and the sub-acquisition module is used for acquiring a predefined sampling area in real time according to a preset time interval to sample the image.
It should be noted that, for the information interaction, execution process, and other contents between the above systems/modules, the specific functions and technical effects of the embodiment of the vehicle compartment-based sampling and monitoring method according to the present application are based on the same concept, and reference may be made to an embodiment of a vehicle compartment-based sampling and monitoring method, which is not described herein again.
It will be clear to those skilled in the art that, for convenience and simplicity of description, the above division of the functional modules is merely illustrated, and in practical applications, the above function allocation may be performed by different functional modules according to needs, that is, the internal structure of the vehicle compartment-based sampling supervision method is divided into different functional modules to perform all or part of the above described functions. Each functional module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional modules are only used for distinguishing one functional module from another, and are not used for limiting the protection scope of the application. For the specific working process of each functional module, reference may be made to the corresponding process in the foregoing embodiment of the vehicle compartment-based sampling and monitoring method, and details are not repeated here.
Fig. 4 is a schematic structural diagram of an electronic device 400 according to a third embodiment of the present application. As shown in fig. 4, the electronic device 400 includes: a processor 402, a memory 401, and a computer program 403 stored in the memory 401 and executable on the processor 402. The number of the processors 402 is at least one, and fig. 4 takes one as an example. The processor 402, when executing the computer program 403, implements the implementation steps of one of the vehicle cabin-based sampling supervision methods described above, i.e., the steps shown in fig. 1 or fig. 2.
The specific implementation process of the electronic device 400 can be seen in the above embodiment of the vehicle compartment-based sampling supervision method.
Illustratively, the computer program 403 may be partitioned into one or more modules/units that are stored in the memory 401 and executed by the processor 402 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 403 in the terminal device 400.
The electronic device 400 may be a desktop computer, a notebook, a palm computer, a main control device, or other computing devices, or may be a camera, a mobile phone, or other devices having an image acquisition function and a data processing function, or may be a touch display device. The electronic device 400 may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that fig. 4 is merely an example of an electronic device 400 and does not constitute a limitation of electronic device 400 and may include more or fewer components than shown, or combine certain components, or different components, e.g., electronic device 400 may also include input-output devices, network access devices, buses, etc.
The Processor 402 may be a CPU (Central Processing Unit), other general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 401 may be an internal storage unit of the electronic device 400, such as a hard disk or a memory. The memory 401 may also be an external storage device of the terminal device 400, such as a plug-in hard disk, SMC (Smart Media Card), SD (Secure Digital Card), Flash Card, or the like provided on the electronic device 400. Further, the memory 401 may also include both an internal storage unit and an external storage device of the electronic device 400. The memory 401 is used for storing an operating system, application programs, a boot loader, data, and other programs, such as program codes of the computer program 403. The memory 401 may also be used to temporarily store data that has been output or is to be output.
Embodiments of the present application further provide a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps in one of the vehicle compartment-based sampling supervision method embodiments above.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the embodiment of the method for monitoring vehicle compartment based sampling described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the embodiment of the method for monitoring vehicle compartment based sampling described above can be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, ROM (Read-Only Memory), RAM (Random Access Memory), electrical carrier wave signal, telecommunication signal, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/system/terminal device and method may be implemented in other ways. For example, the above-described apparatus/system/terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices/systems or units through some interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (11)
1. A vehicle compartment-based sampling supervision method, characterized in that the method comprises:
acquiring position information of the vehicle compartment according to a sampling image corresponding to a sampling area acquired in real time;
and carrying out sampling supervision on the vehicle compartment according to the position information and a preset sampling rule.
2. The method according to claim 1, wherein the obtaining the position information of the vehicle compartment according to the sampling image corresponding to the sampling area acquired in real time comprises:
carrying out binarization processing on the sampling image, and calculating gray information of the sampling image;
extracting a contour region of the vehicle compartment from the sampling image according to the gray information;
and acquiring the position information of the vehicle compartment according to the contour area.
3. The method of claim 2, wherein said extracting a contour region of the vehicle cabin from the sampled image based on the gray scale information comprises:
comparing the gray information of the sampled image with a gray threshold value to obtain a comparison result;
and determining a contour area corresponding to the vehicle compartment according to the comparison result.
4. The method according to claim 2, wherein the obtaining the position information of the vehicle compartment according to the contour region specifically comprises:
drawing a minimum bounding rectangle based on the outline region;
and acquiring the position information of the vehicle compartment according to the minimum circumscribed rectangle, wherein the position information comprises four vertex coordinates of the minimum circumscribed rectangle.
5. The method of claim 4, wherein the vehicle cabin is sampled and supervised according to the location information and a preconfigured sampling rule, the method comprising:
equally dividing the minimum circumscribed rectangle into a preset number of sub-sampling regions;
and calling a pre-configured sampling rule, and carrying out sampling supervision on the sub-sampling area according to the sampling rule.
6. The method of claim 5, wherein prior to said invoking a preconfigured sampling rule and sampling supervision of said sub-sampled region according to said sampling rule, said method further comprises:
acquiring coordinates of sampling equipment and coordinates of the sub-sampling area;
correspondingly, the calling a pre-configured sampling rule and performing sampling supervision on the sub-sampling area according to the sampling rule specifically includes:
and carrying out sampling supervision on the sampling of the target sub-sampling area according to the coordinates of the sampling equipment and the coordinates of the sub-sampling area, and the target sub-sampling area or the target sub-sampling area and the sampling sequence defined by the sampling rule.
7. The method according to claim 6, wherein the monitoring of sampling of the target sub-sampling region according to the coordinates of the sampling device and the coordinates of the sub-sampling region, and the target sub-sampling region and the sampling order defined by the sampling rule comprises:
judging whether the coordinates of the sampling equipment fall into the coordinate range of the sub-sampling area or not;
if yes, judging that the sample is normal;
if not, the sampling is judged to be abnormal.
8. The method according to any one of claims 1 to 7, wherein the position information of the vehicle compartment is acquired in the sample image corresponding to the sample area acquired in real time, and the method further comprises:
and acquiring a sampling image corresponding to the sampling area according to a preset time interval.
9. A vehicle compartment-based sampling surveillance system, the system comprising:
the acquisition module is used for acquiring the position information of the vehicle compartment according to the sampling image corresponding to the sampling area acquired in real time;
and the sampling supervision module is used for sampling and supervising the vehicle compartment according to the position information and a preset sampling rule.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements a vehicle compartment-based sampling supervision method according to any one of claims 1-8.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements a vehicle compartment-based sampling supervision method according to any one of claims 1 to 8.
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| CN202011535761.9A CN112633165A (en) | 2020-12-23 | 2020-12-23 | Vehicle compartment-based sampling supervision method and system, storage medium and electronic equipment |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108133611A (en) * | 2016-12-01 | 2018-06-08 | 中兴通讯股份有限公司 | Vehicle driving trace monitoring method and system |
| CN109211607A (en) * | 2018-09-25 | 2019-01-15 | 中铝视拓智能科技有限公司 | A kind of method of sampling, device, equipment, system and readable storage medium storing program for executing |
| CN109389082A (en) * | 2018-09-30 | 2019-02-26 | 北京旷视科技有限公司 | Sight acquisition method, device, system, computer readable storage medium |
| WO2019080055A1 (en) * | 2017-10-26 | 2019-05-02 | 深圳市锐明技术股份有限公司 | Compartment state detection method and compartment state detection device for transport vehicle and terminal |
| CN110569699A (en) * | 2018-09-07 | 2019-12-13 | 阿里巴巴集团控股有限公司 | Method and device for carrying out target sampling on picture |
| CN110619329A (en) * | 2019-09-03 | 2019-12-27 | 中国矿业大学 | Carriage number and loading state identification method of railway freight open wagon based on airborne vision |
-
2020
- 2020-12-23 CN CN202011535761.9A patent/CN112633165A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108133611A (en) * | 2016-12-01 | 2018-06-08 | 中兴通讯股份有限公司 | Vehicle driving trace monitoring method and system |
| WO2019080055A1 (en) * | 2017-10-26 | 2019-05-02 | 深圳市锐明技术股份有限公司 | Compartment state detection method and compartment state detection device for transport vehicle and terminal |
| CN110569699A (en) * | 2018-09-07 | 2019-12-13 | 阿里巴巴集团控股有限公司 | Method and device for carrying out target sampling on picture |
| CN109211607A (en) * | 2018-09-25 | 2019-01-15 | 中铝视拓智能科技有限公司 | A kind of method of sampling, device, equipment, system and readable storage medium storing program for executing |
| CN109389082A (en) * | 2018-09-30 | 2019-02-26 | 北京旷视科技有限公司 | Sight acquisition method, device, system, computer readable storage medium |
| CN110619329A (en) * | 2019-09-03 | 2019-12-27 | 中国矿业大学 | Carriage number and loading state identification method of railway freight open wagon based on airborne vision |
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