CN117765425A - Refrigerator counting method and device, server and storage medium - Google Patents
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Abstract
The application discloses a refrigerator counting method, a refrigerator counting device, a server and a storage medium; the method comprises the following steps: extracting at least one image to be detected from the monitoring video; inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators; and counting the goods of the category according to the category of the goods in each image to be detected. According to the embodiment of the application, the refrigerator on the conveying belt can be automatically counted in real time, so that a large amount of labor cost can be saved, and the counting accuracy can be improved.
Description
Technical Field
The embodiment of the application relates to the technical field of industrial control, in particular to a refrigerator counting method, a refrigerator counting device, a server and a storage medium.
Background
In order to respond to the call of national energy conservation and emission reduction and recycling of waste articles, a plurality of companies take measures of disassembling and recycling the waste household appliances. On the disassembly assembly line of the old refrigerator, in order to ensure the quantity of warehouse entry, warehouse exit and verification, the complete machine counting of the household appliances is required to be carried out on the disassembly assembly line.
The recycling process of plastic parts in a refrigerator cabinet generally comprises: foreign matter such as metal, rubber, glass, etc. attached to the plastic product is removed first, and then the plastic component is classified in terms of material, color, etc. and placed in a container.
The existing refrigerator counting method is generally as follows: a forklift driver holds a plurality of waste refrigerators by using an iron frame, and pipeline conveying workers carry the refrigerators to a conveyor belt one by one, so that refrigerator counting is performed. But this method not only wastes a lot of labor costs, but also may count inaccurately, easily causing repetition, neglect and errors.
Disclosure of Invention
The refrigerator counting method, the refrigerator counting device, the server and the storage medium can automatically count the refrigerators on the conveying belt in real time, so that a large amount of labor cost can be saved, and the counting accuracy can be improved.
In a first aspect, an embodiment of the present application provides a method for counting a refrigerator, including:
extracting at least one image to be detected from the monitoring video;
inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the categories include: refrigerators and non-refrigerators;
and counting the goods in the category according to the category of the goods in each image to be detected.
In a second aspect, embodiments of the present application further provide a refrigerator counting device, including: the device comprises an extraction module, a determination module and a counting module; wherein,
the extraction module is used for extracting at least one image to be detected from the monitoring video;
the determining module is used for inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the categories include: refrigerators and non-refrigerators;
and the counting module is used for counting the goods in the category according to the category of the goods in each image to be detected.
In a third aspect, an embodiment of the present application provides a server, including:
one or more processors;
a memory for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the refrigerator counting method described in any embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a storage medium having stored thereon a computer program which, when executed by a processor, implements the refrigerator counting method described in any embodiment of the present application.
The embodiment of the application provides a refrigerator counting method, a refrigerator counting device, a refrigerator counting server and a refrigerator storage medium, wherein at least one image to be detected is extracted from a monitoring video; then inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators; and then counting the classified cargos according to the classified cargos in each image to be detected. That is, in the technical solution of the present application, the refrigerators in the monitoring video may be counted through a pre-trained detection model. In the prior art, the line carrier carries the refrigerators one by one to the conveyor belt, and in this way, the refrigerator counting is performed. But this method not only wastes a lot of labor costs, but also may count inaccurately, easily causing repetition, neglect and errors. Therefore, compared with the prior art, the refrigerator counting method, device, server and storage medium provided by the embodiment of the application can automatically count the refrigerators on the conveying belt in real time, so that a great deal of labor cost can be saved, and the counting accuracy can be improved; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
Drawings
Fig. 1 is a schematic flow chart of a refrigerator counting method according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a second flow of the refrigerator counting method according to the embodiment of the present application;
fig. 3 is a schematic view of a third flow chart of a refrigerator counting method according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a refrigerator counting device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a schematic flow chart of a refrigerator counting method according to an embodiment of the present application, where the method may be performed by a refrigerator counting device or a server, and the device or the server may be implemented by software and/or hardware, and the device or the server may be integrated in any intelligent device having a network communication function. As shown in fig. 1, the refrigerator counting method may include the steps of:
s101, at least one image to be detected is extracted from the monitoring video.
In this step, the server may first acquire the monitoring video; at least one image to be detected is then extracted from the monitored video. The server can extract N images to be detected from the monitoring video; wherein N is a natural number greater than or equal to 1.
S102, inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators.
In the step, the server can input each image to be detected into a pre-trained detection model, and the category of goods in each image to be detected is determined through the detection model; wherein the category includes: refrigerators and non-refrigerators. Specifically, the server can determine whether each image to be detected is a refrigerator or goods outside the refrigerator through the detection model.
S103, counting the cargoes according to the category of the cargoes in each image to be detected.
In this step, the server may count the types of goods in each image to be detected according to the types of the goods. Specifically, if the goods in the current image to be detected are refrigerators, adding 1 to the count of the refrigerators; if the goods in the current image to be detected are the goods outside the refrigerator, adding 1 to the count of the non-refrigerator.
The refrigerator counting method provided by the embodiment of the application comprises the steps of firstly extracting at least one image to be detected from a monitoring video; then inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators; and then counting the classified cargos according to the classified cargos in each image to be detected. That is, in the technical solution of the present application, the refrigerators in the monitoring video may be counted through a pre-trained detection model. In the prior art, the line carrier carries the refrigerators one by one to the conveyor belt, and in this way, the refrigerator counting is performed. But this method not only wastes a lot of labor costs, but also may count inaccurately, easily causing repetition, neglect and errors. Therefore, compared with the prior art, the refrigerator counting method provided by the embodiment of the application can automatically count the refrigerators on the conveying belt in real time, so that a great amount of labor cost can be saved, and the counting accuracy can be improved; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
Example two
Fig. 2 is a schematic diagram of a second flow chart of a refrigerator counting method according to an embodiment of the present application. Further optimization and expansion based on the above technical solution can be combined with the above various alternative embodiments. As shown in fig. 2, the refrigerator counting method may include the steps of:
and S201, labeling N original images acquired in advance to obtain data combinations corresponding to each original image.
In this step, the server may label N original images acquired in advance, to obtain a data combination corresponding to each original image. The data combination in the embodiment of the application may include: original image and text data; the text data includes: the category of goods in each original image and the position of the target detection frame in each original image; the positions include an upper left corner and a lower right corner of the target detection frame. For example, a refrigerator in an original image can be marked by a detection frame, and the coordinates of the upper left corner and the lower right corner of the detection frame can be respectively expressed as (x 1, y 1) and (x 2, y 2), and the two coordinates and the cargo type jointly form text data in a data combination corresponding to the original image.
S202, training a detection model to be trained by using the data combination corresponding to each original image.
In this step, the server may train the detection model to be trained using the data combination corresponding to each original image. Specifically, if the detection model to be trained does not meet the preset convergence condition, the server may extract one data combination from the data combinations corresponding to the N original images as the current data combination; and then training the detection model to be trained by taking the current data combination as a training sample, and repeatedly executing the operation until the detection model to be trained meets the convergence condition.
Further, the server may input the current data combination into the detection model to be trained, and output a detection result of the current data combination through the detection model to be trained; then calculating a loss value corresponding to the current data combination through a predetermined loss function according to the detection result of the current data combination and the text data in the predetermined current data combination; if the loss value corresponding to the current data combination is within a preset range, the server can judge that the detection model to be trained meets the convergence condition; if the loss value corresponding to the current data combination is not in the preset range, the server can judge that the detection model to be trained does not meet the convergence condition.
S203, at least one image to be detected is extracted from the monitoring video.
S204, inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators.
S205, counting the cargoes according to the category of the cargoes in each image to be detected.
The refrigerator counting method provided by the embodiment of the application comprises the steps of firstly extracting at least one image to be detected from a monitoring video; then inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators; and then counting the classified cargos according to the classified cargos in each image to be detected. That is, in the technical solution of the present application, the refrigerators in the monitoring video may be counted through a pre-trained detection model. In the prior art, the line carrier carries the refrigerators one by one to the conveyor belt, and in this way, the refrigerator counting is performed. But this method not only wastes a lot of labor costs, but also may count inaccurately, easily causing repetition, neglect and errors. Therefore, compared with the prior art, the refrigerator counting method provided by the embodiment of the application can automatically count the refrigerators on the conveying belt in real time, so that a great amount of labor cost can be saved, and the counting accuracy can be improved; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
Example III
Fig. 3 is a schematic view of a third flow chart of a refrigerator counting method according to an embodiment of the present application. Further optimization and expansion based on the above technical solution can be combined with the above various alternative embodiments. As shown in fig. 3, the refrigerator counting method may include the steps of:
and S301, labeling N original images acquired in advance to obtain data combinations corresponding to each original image.
S302, if a detection model to be trained does not meet a preset convergence condition, extracting one data combination from data combinations corresponding to N original images to serve as a current data combination.
And S303, training the detection model to be trained by taking the current data combination as a training sample until the detection model to be trained meets the convergence condition.
In this step, the server may train the detection model to be trained using the current data combination as a training sample until the detection model to be trained meets the convergence condition. The data combination in the embodiment of the application may include: original image and text data; the text data includes: the category of goods in each original image and the position of the target detection frame in each original image; the positions include an upper left corner and a lower right corner of the target detection frame.
S304, at least one image to be detected is extracted from the monitoring video.
S305, inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators.
S306, counting the types of cargos according to the types of the cargos in each image to be detected.
Preferably, in a specific embodiment of the present application, before training a detection model to be trained by using a current data combination as a training sample, the server may further determine whether pixels of an original image in the current data combination meet a predetermined requirement; if the pixels of the original image do not meet the predetermined requirement, the server may adjust the pixels of the original image so that the pixels of the original image meet the predetermined requirement.
Preferably, in the embodiment of the present application, before labeling N pre-acquired original images, it may also be detected whether a hardware device in the server meets a preset configuration requirement; if the hardware equipment in the server does not meet the configuration requirement, the hardware equipment in the server can be reconfigured so that the hardware equipment in the server meets the configuration requirement; wherein the hardware devices include, but are not limited to: memory, graphics card, CPU, GPU.
The technical scheme provided by the application is divided into an offline part and an online part; the off-line part is off-line training of the detection and identification model, and the on-line part is on-line testing of workshop production line videos and pictures. The method specifically comprises the following steps: 1) Framing images collected under different cameras and performing label processing; 2) The target detection module performs feature extraction and processing on the image information through a corresponding algorithm, and establishes a detection and classification model for disassembling the scene image; 3) After the model is trained offline and deployed, a background detection classification module is used for detecting and identifying new test videos online, and a rule judgment algorithm such as tripwire detection is called to detect, count and identify household appliances, core components, smoke and the like; 4) The Web back end invokes the background identified structured json and transmits and stores the data; 5) And the Web front end performs visual display and application layer operation on the background data to complete the intelligent analysis system for the dismantling video of the waste household appliances.
In the specific embodiment of the application, after the data set is prepared and the model structure is determined, the model can be trained, and the task of automatically detecting and identifying the image is completed. The test may run on a server with a CPU i7-8750H, memory 16g,240g ssd, gpu single card NVIDIA 1050. The parallel computing characteristic of the GPU is adopted to accelerate, and the model training speed is far higher than that of an independent CPU platform by matching CUDA11.1 and a matched deep neural network library (cuDNN).
In the training process, two main parameters for measuring the accuracy of the training model are the accuracy of the current loss and the verification set. In order to train to obtain the optimal training model, the over fitting phenomenon does not occur, and the final loss function is close to 0. In the training process, under the condition that the initial learning rate is 0.01, the degree of the increase of the iteration times gradually decreases, and the final verification accuracy is stabilized at about 95.3%.
According to the embodiment of the application, the refrigerator on the belt is subjected to target detection and counting in a video monitoring mode. The method is characterized in that video supervision, image processing, a deep neural network and a tripwire detection algorithm are integrated, and first, information of a first site is mastered simply and clearly in a video mode; then decoding video and framing by video decoding technology; carrying out real-time target detection on the waste refrigerator on each frame of image; and then, performing tripwire analysis and counting on the target center point, structuring the identification content, and performing visual display on the Web front end. The automatic counting device can realize automatic counting, and labor cost is saved. The method and the device can increase the back end and the front end of Web, and can perform data management and workflow visualization.
The refrigerator counting method provided by the embodiment of the application comprises the steps of firstly extracting at least one image to be detected from a monitoring video; then inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the category includes: refrigerators and non-refrigerators; and then counting the classified cargos according to the classified cargos in each image to be detected. That is, in the technical solution of the present application, the refrigerators in the monitoring video may be counted through a pre-trained detection model. In the prior art, the line carrier carries the refrigerators one by one to the conveyor belt, and in this way, the refrigerator counting is performed. But this method not only wastes a lot of labor costs, but also may count inaccurately, easily causing repetition, neglect and errors. Therefore, compared with the prior art, the refrigerator counting method provided by the embodiment of the application can automatically count the refrigerators on the conveying belt in real time, so that a great amount of labor cost can be saved, and the counting accuracy can be improved; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
Example IV
Fig. 4 is a schematic structural diagram of a refrigerator counting device according to an embodiment of the present application. As shown in fig. 4, the refrigerator counting device includes: an extraction module 401, a determination module 402, and a counting module 403; wherein,
the extracting module 401 is configured to extract at least one image to be detected from a monitoring video acquired in advance;
the determining module 402 is configured to input each image to be detected into a pre-trained detection model, and determine a category of goods in each image to be detected through the detection model; wherein the categories include: refrigerators and non-refrigerators;
the counting module 403 is configured to count the types of cargos according to the types of the cargos in each image to be detected.
The refrigerator counting device can execute the method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment can be seen in the refrigerator counting method provided in any embodiment of the present application.
Example five
Fig. 5 shows a schematic diagram of the architecture of a server 10 that may be used to implement an embodiment of the present invention. Servers are intended to represent various forms of digital computers, such as laptops, desktops, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The server may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the server 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the server 10 can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
The various components in the server 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the server 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as a refrigerator counting method.
In some embodiments, the refrigerator counting method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the server 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the refrigerator counting method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the refrigerator counting method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on a server having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the server. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A refrigerator counting method, applied to a server, the method comprising:
extracting at least one image to be detected from the monitoring video;
inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the categories include: refrigerators and non-refrigerators;
and counting the goods in the category according to the category of the goods in each image to be detected.
2. The method of claim 1, wherein prior to extracting at least one frame of image from the pre-acquired video, the method further comprises:
labeling N original images acquired in advance to obtain a data combination corresponding to each original image;
and training the detection model to be trained by using the data combination corresponding to each original image.
3. The method of claim 2, wherein the data combining comprises: original image and text data; the text data includes: the category of goods in each original image and the position of the target detection frame in each original image; the positions include an upper left corner and a lower right corner of the target detection frame.
4. A method according to claim 3, wherein training the detection model to be trained using the data combination corresponding to each original image comprises:
if the detection model to be trained does not meet the preset convergence condition, extracting one data combination from the data combinations corresponding to the N original images as the current data combination;
and training the detection model to be trained by taking the current data combination as a training sample, and repeatedly executing the operation until the detection model to be trained meets the convergence condition.
5. The method of claim 4, wherein training the test model to be trained using the current data combination as training samples comprises:
inputting the current data combination into the detection model to be trained, and outputting a detection result of the current data combination through the detection model to be trained;
calculating a loss value corresponding to the current data combination through a predetermined loss function according to the detection result of the current data combination and predetermined text data in the current data combination;
and if the loss value corresponding to the current data combination is within a preset range, judging that the detection model to be trained meets the convergence condition.
6. The method of claim 2, wherein prior to training the detection model to be trained using the current data combination as training samples, the method further comprises:
judging whether the pixels of the original image in the current data combination meet the preset requirement or not;
and if the pixels of the original image do not meet the preset requirements, adjusting the pixels of the original image so that the pixels of the original image meet the preset requirements.
7. The method of claim 2, wherein prior to labeling the pre-acquired N original images, the method further comprises:
detecting whether hardware equipment in the server meets preset configuration requirements or not;
if the hardware equipment in the server does not meet the configuration requirement, reconfiguring the hardware equipment in the server to enable the hardware equipment in the server to meet the configuration requirement; wherein the hardware device includes, but is not limited to: memory, graphics card, CPU, GPU.
8. A refrigerator counter, the device comprising: the device comprises an extraction module, a determination module and a counting module; wherein,
the extraction module is used for extracting at least one image to be detected from a monitoring video which is acquired in advance;
the determining module is used for inputting each image to be detected into a pre-trained detection model, and determining the category of goods in each image to be detected through the detection model; wherein the categories include: refrigerators and non-refrigerators;
and the counting module is used for counting the goods in the category according to the category of the goods in each image to be detected.
9. A server, comprising:
one or more processors;
a memory for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the refrigerator counting method of any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program which when executed by a processor implements the refrigerator counting method according to any one of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211135831.0A CN117765425A (en) | 2022-09-19 | 2022-09-19 | Refrigerator counting method and device, server and storage medium |
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| CN202211135831.0A CN117765425A (en) | 2022-09-19 | 2022-09-19 | Refrigerator counting method and device, server and storage medium |
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