CN118368445A - Image detection method, device and related equipment - Google Patents
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/232—Content retrieval operation locally within server, e.g. reading video streams from disk arrays
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Abstract
The application provides an image detection method, an image detection device and related equipment, wherein the method comprises the following steps: storing a plurality of images to be detected in a first server to generate a plurality of address information; generating an address queue according to a processing request sent by a user side; and acquiring the n target images to be detected in the first server based on the address queue, and performing image detection on the n target images to be detected to generate n detection results. According to the application, the images to be detected are stored in the first server, in the process that n target images to be detected in the images to be detected need to be detected, n target images to be detected are downloaded through n pieces of address information, and then the n target images to be detected are detected, so that the situation that a large number of pictures occupy space is avoided, and the efficiency of image detection is improved.
Description
Technical Field
The present invention relates to the field of computer vision, and in particular, to an image detection method, apparatus and related device.
Background
Pedestrian recognition technology has gained widespread attention and application in recent years. With increasing demands of urban traffic and public safety, real-time large-scale reasoning on pedestrian targets becomes an important problem, and particularly in public places such as supermarkets, subway stations, bus stations and railway stations, the demands on personnel counting are very high. In the prior art, images are generally directly downloaded and processed in the process of identifying pedestrians, and the processing speed of directly identifying the images is low due to the fact that a large amount of images and video data are required to be processed, so that the problem of low efficiency of image detection in the prior art is caused.
Disclosure of Invention
The embodiment of the application provides an image detection method, an image detection device and related equipment, which solve the problem of lower image detection efficiency in the prior art.
In a first aspect, an embodiment of the present application provides an image detection method, including:
storing a plurality of images to be detected in a first server to generate a plurality of address information, wherein the images to be detected correspond to the address information one by one;
Generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the plurality of images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and the n is a positive integer;
Acquiring the n target to-be-detected images in a first server based on the address queue, performing image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of target objects included in the corresponding target to-be-detected images.
Optionally, the obtaining the n target images to be detected in the first server based on the address queue, and performing image detection on the n target images to be detected, to generate n detection results, includes:
Acquiring the n target images to be detected from the first server based on the address queue;
and sequentially inputting the n target images to be detected into a target image detection model for image detection, and generating the n detection results, wherein the target image detection model is used for detecting the number of the target objects in the input images.
Optionally, the n target images to be detected are sequentially input into a target image detection model to perform image detection, and before the n detection results are generated, the method further includes:
Acquiring a training data set, wherein the training data set comprises a plurality of training data, each training data comprises a sample image and label information corresponding to the sample image, the plurality of label information corresponds to the plurality of sample images one by one, the sample images comprise at least one image of the target object, and the label information is used for indicating the number of the target objects included in the corresponding sample images;
and training the image detection model by using the training data set to obtain the target image detection model.
Optionally, the sequentially inputting the n target images to be detected into a target image detection model for image detection, generating the n detection results includes:
Obtaining m target image detection models, wherein m is a positive integer;
And distributing the n target images to be detected to the m target image detection models according to a preset rule for image detection, and generating the n detection results.
Optionally, the method further includes, after acquiring the n target images to be detected in the first server based on the address queue and performing image detection on the n target images to be detected, generating n detection results:
storing the n detection results in a second server;
receiving a data query request sent by the user side, wherein the data query request is used for querying the n detection results;
Downloading target data in the second server based on the data query request, wherein the target data comprises the n detection results;
and sending the target data to the user terminal.
Optionally, before storing the plurality of images to be detected in the first server and generating the plurality of address information, the method further includes:
Acquiring a plurality of videos to be detected, wherein the videos to be detected are videos shot for a target area, and the shooting angles of different videos to be detected in the videos to be detected are different;
and respectively carrying out format conversion on the plurality of videos to be detected to generate the plurality of images to be detected.
In a second aspect, an embodiment of the present application further provides an image detection apparatus, including:
The storage module is used for storing a plurality of images to be detected in the first server and generating a plurality of address information, and the images to be detected are in one-to-one correspondence with the address information;
The generating module is used for generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the plurality of images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and n is a positive integer;
The acquisition module is used for acquiring the n target to-be-detected images in the first server based on the address queue, carrying out image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of the target objects included in the corresponding target to-be-detected images.
In a third aspect, an embodiment of the present application further provides a communication device, including: a transceiver, a memory, a processor, and a program stored on the memory and executable on the processor; the processor is configured to read a program in the memory to implement the steps in the method according to the foregoing first aspect.
In a fourth aspect, embodiments of the present application also provide a readable storage medium storing a program which, when executed by a processor, implements the steps of the method as described in the foregoing first aspect.
In a fifth aspect, embodiments of the present application also provide a computer program product stored in a storage medium, the computer program product being executable by at least one processor to implement the steps in the method according to the first aspect.
The application provides an image detection method, an image detection device and related equipment, wherein the method comprises the following steps: storing a plurality of images to be detected in a first server to generate a plurality of address information, wherein the images to be detected correspond to the address information one by one; generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the plurality of images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and the n is a positive integer; acquiring the n target to-be-detected images in a first server based on the address queue, performing image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of target objects included in the corresponding target to-be-detected images. According to the application, the images to be detected are stored in the first server, in the process that n target images to be detected in the images to be detected need to be detected, n target images to be detected are downloaded through n pieces of address information, and then the n target images to be detected are detected, so that the situation that a large number of pictures occupy space is avoided, and the efficiency of image detection is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a schematic flow chart of an image detection method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an image detection device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," and the like in embodiments of the present application are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. 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. Furthermore, the use of "and/or" in the present application means at least one of the connected objects, such as a and/or B and/or C, means 7 cases including a alone a, B alone, C alone, and both a and B, both B and C, both a and C, and both A, B and C.
Referring to fig. 1, fig. 1 is a flowchart of an image detection method according to an embodiment of the present application. The image detection method shown in fig. 1 includes the steps of:
step 101, storing a plurality of images to be detected in a first server, and generating a plurality of address information, wherein the images to be detected are in one-to-one correspondence with the address information.
In this embodiment, the first server is a server for storing pictures, and the plurality of images to be detected may be captured by the plurality of cameras to obtain a video stream, and image conversion is performed on the video stream to generate the video stream. It should be noted that the plurality of images to be detected may be images captured in a certain target area, for example, images of pedestrians captured in a certain street, or images of pedestrians captured in a certain building.
The address information is a storage address of each image to be detected stored in the first server, and it is to be noted that the storage addresses corresponding to each image to be detected are different, and in addition, each image to be detected corresponds to an independent uuid, i.e. identification information, through which different images and storage positions of different images can be accurately distinguished.
Step 102, generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the multiple images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and n is a positive integer.
In this embodiment, the user terminal may be a terminal of a user that needs to perform detection, for example, a computer, a mobile phone, and so on. Generating an address queue through a processing request generated by a user side, wherein the processing request comprises n uuid which is identification information of n target images to be detected to be processed, n is a positive integer, when n is equal to the number of the multiple images to be detected, all the images to be detected are detected, or a part of the images to be detected can be detected according to user requirements, and the embodiment is not limited specifically.
In addition, the address queue is the address information of n target images to be detected, and n target images to be detected can be correspondingly downloaded and acquired in the first server through the address queue. In the prior art, all images are generally downloaded and then processed, but in the application, after the images needing to be detected are determined, the images are downloaded to the first server, so that the cache pressure of the local server can be reduced, and the processing efficiency of the images is improved.
Step 103, acquiring the n target to-be-detected images in a first server based on the address queue, performing image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of the target objects included in the corresponding target to-be-detected images.
In this embodiment, n target images to be detected are acquired in the first server through the address queue, so that image detection is performed on the n target images to be detected, specifically, the image detection may be performed by using a trained image detection model, for example, in this embodiment, n target images to be detected may be detected by simultaneously setting a plurality of image detection models, so that detection efficiency may be improved.
The application provides an image detection method, which comprises the following steps: storing a plurality of images to be detected in a first server to generate a plurality of address information, wherein the images to be detected correspond to the address information one by one; generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the plurality of images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and the n is a positive integer; acquiring the n target to-be-detected images in a first server based on the address queue, performing image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of target objects included in the corresponding target to-be-detected images. According to the application, the images to be detected are stored in the first server, in the process that n target images to be detected in the images to be detected need to be detected, n target images to be detected are downloaded through n pieces of address information, and then the n target images to be detected are detected, so that the situation that a large number of pictures occupy space is avoided, and the efficiency of image detection is improved.
In some possible implementations, optionally, the obtaining the n target images to be detected in the first server based on the address queue, and performing image detection on the n target images to be detected, to generate n detection results includes:
Acquiring the n target images to be detected from the first server based on the address queue;
and sequentially inputting the n target images to be detected into a target image detection model for image detection, and generating the n detection results, wherein the target image detection model is used for detecting the number of the target objects in the input images.
In this embodiment, the target image detection model is a trained image detection model, wherein the image detection model is an artificial intelligence model, which is intended to identify different objects, objects or scenes in the image and mark their positions. Such models typically use computer vision techniques, such as Convolutional Neural Networks (CNNs), to learn image features and detect and identify objects in the image from these features. Image detection models are widely used in many fields including autopilot, security monitoring, medical image analysis, and the like.
In this embodiment, the target object is described by taking a pedestrian as an example, wherein after the target image detection model is trained by a large amount of training data, pedestrian detection can be performed on the input image, for example, the number of pedestrians in the image is detected. For example, 3 target images to be detected are detected respectively, wherein the number of pedestrians is 5, 6 and 8 respectively, so that three detection results are generated respectively, and the detection results correspond to different target images to be detected, namely, the detection results can be distinguished by uuid of the target images to be detected.
Optionally, the n target images to be detected are sequentially input into a target image detection model to perform image detection, and before the n detection results are generated, the method further includes:
Acquiring a training data set, wherein the training data set comprises a plurality of training data, each training data comprises a sample image and label information corresponding to the sample image, the plurality of label information corresponds to the plurality of sample images one by one, the sample images comprise at least one image of the target object, and the label information is used for indicating the number of the target objects included in the corresponding sample images;
and training the image detection model by using the training data set to obtain the target image detection model.
In this embodiment, the training data set is used to train the image detection model to obtain the target image detection model. Specifically, the image detection model is trained through a training data set, wherein the training data set comprises a plurality of training data, each training data set comprises a sample image and label information corresponding to the sample image, and the label information is the number of target objects included in the sample image, such as the number of pedestrians. Compared with manual detection and identification, the image detection efficiency and accuracy can be improved through the image detection model.
And training the image detection model through the training data set until the loss value of the image detection model is lower than a preset threshold value, thereby obtaining the target image detection model after training.
Optionally, the sequentially inputting the n target images to be detected into a target image detection model for image detection, generating the n detection results includes:
Obtaining m target image detection models, wherein m is a positive integer;
And distributing the n target images to be detected to the m target image detection models according to a preset rule for image detection, and generating the n detection results.
In this embodiment, the trained target image detection model is obtained in the above embodiment, and the target image detection model is expanded into m target image detection models, that is, the m target detection models can be used to perform image detection on n target images to be detected at the same time, so as to generate n detection results, where the m target image detection models can be set in the same server and perform image detection in parallel, so that the detection efficiency is improved.
By way of example, pictures may be inferred in a multi-process parallel reasoning manner. The reasoning uses 3090 graphics cards, and each 3090 graphics card can run 4 reasoning processes simultaneously. Each process can simultaneously infer 64 pictures. And a Redis transmission message queue is adopted to distribute the messages to different processes, so that the congestion condition is avoided. The pictures are inferred by adopting a multi-process parallel reasoning mode, so that the real-time reasoning of 256 pictures at most can be realized by using a single Zhang Xianka, and the reasoning speed is not more than 0.5 seconds. The time delay of reasoning is greatly reduced. The use of the references to control the distribution and delivery of messages reduces the problem of congestion.
It should be noted that the present application may be applied to Flask service ends, where Flask service ends are high concurrency service frames based on multiple processes and coordinated processes, and multiple processes may be flexibly configured according to the number of picture messages. The picture message can reach the service end Flask in real time. Because the reasoning service is multiprocessing, flask service end does not send collected picture information to the reasoning service directly, wherein the reasoning service is to detect the image, generate uuid for each picture information, thus guaranteeing uniqueness of the picture information, and then send the image information and the corresponding uuid to the references queue. After detecting that the information exists in the Reids, the reasoning service takes out the information of the Reids, finds the path of the picture from the taken information, and loads picture reasoning. The inferred results and the corresponding uuid are put back to the Reids. Flask the server compares the received result with the sent uuid, finds a consistent uuid, takes out the result from the references, and returns the result. And Flask, after receiving the result returned by the reasoning server, the server saves the result into a time sequence database for subsequent analysis and processing. And on the other hand, pushing the result to the front end for real-time display.
Optionally, the method further includes, after acquiring the n target images to be detected in the first server based on the address queue and performing image detection on the n target images to be detected, generating n detection results:
storing the n detection results in a second server;
receiving a data query request sent by the user side, wherein the data query request is used for querying the n detection results;
Downloading target data in the second server based on the data query request, wherein the target data comprises the n detection results;
and sending the target data to the user terminal.
In this embodiment, the second server is a data storage server, where when the user side needs to query the data, a data query request is generated. After receiving the data query request, downloading target data in the second server through the data query request, wherein the target data comprises n detection results, and feeding the target data back to the user side after receiving the target data sent by the second server.
Optionally, before storing the plurality of images to be detected in the first server and generating the plurality of address information, the method further includes:
Acquiring a plurality of videos to be detected, wherein the videos to be detected are videos shot for a target area, and the shooting angles of different videos to be detected in the videos to be detected are different;
and respectively carrying out format conversion on the plurality of videos to be detected to generate the plurality of images to be detected.
In this embodiment, a stream fetching program is deployed by using raspberry groups, each raspberry group supports 4 paths of cameras to fetch streams, and the fetched streams are converted into pictures and stored in a picture server. Specifically, the target area can be shot by a plurality of cameras with different angles, so that a plurality of videos to be detected are obtained. And simultaneously, transferring the path of the picture to a message queue. According to the number of cameras, a flow-out program is flexibly deployed and easily expanded. Therefore, a streaming mode of distributed multi-process is adopted. The speed of taking the stream is greatly submitted, and the delay is controlled to be about 200 ms.
According to the application, the images to be detected are stored in the first server, in the process that n target images to be detected in the images to be detected need to be detected, n target images to be detected are downloaded through n pieces of address information, and then the n target images to be detected are detected, so that the situation that a large number of pictures occupy space is avoided, and the efficiency of image detection is improved.
Referring to fig. 2, fig. 2 is a block diagram of an image detection apparatus according to an embodiment of the present application. As shown in fig. 2, the image detection apparatus 200 includes:
The storage module 210 is configured to store a plurality of images to be detected in the first server, and generate a plurality of address information, where the plurality of images to be detected are in one-to-one correspondence with the plurality of address information;
The generating module 220 is configured to generate an address queue according to a processing request sent by a user side, where the processing request includes identification information of n target images to be detected, the n target images to be detected are n images to be detected in the multiple images to be detected, the address queue includes address information corresponding to the n target images to be detected, and n is a positive integer;
The obtaining module 230 is configured to obtain the n target to-be-detected images in the first server based on the address queue, perform image detection on the n target to-be-detected images, and generate n detection results, where the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used to indicate the number information of the target objects included in the corresponding target to-be-detected images.
Optionally, the obtaining module 230 includes:
The first acquisition submodule is used for acquiring the n target images to be detected in the first server based on the address queue;
The generation sub-module is used for sequentially inputting the n target images to be detected into a target image detection model for image detection, generating the n detection results, and the target image detection model is used for detecting the number of the target objects in the input images.
Optionally, the method further comprises:
A second obtaining sub-module, configured to obtain a training data set, where the training data set includes a plurality of training data, each training data includes one sample image and tag information corresponding to the sample image, the plurality of tag information corresponds to the plurality of sample images one to one, the sample image is an image including at least one target object, and the tag information is used to indicate a number of target objects included in the corresponding sample image;
and the training sub-module is used for training the image detection model by using the training data set to obtain the target image detection model.
Optionally, the generating submodule includes:
the acquisition unit is used for acquiring m target image detection models, wherein m is a positive integer;
the detection unit is used for distributing the n target images to be detected to the m target image detection models according to a preset rule to carry out image detection and generating the n detection results.
Optionally, the method further comprises:
The result storage module is used for storing the n detection results in the second server;
The receiving module is used for receiving a data query request sent by the user side, wherein the data query request is used for querying the n detection results;
the downloading module is used for downloading target data in the second server based on the data query request, wherein the target data comprises the n detection results;
and the sending module is used for sending the target data to the user terminal.
Optionally, the method further comprises:
The video acquisition module is used for acquiring a plurality of videos to be detected, wherein the videos to be detected are videos shot aiming at a target area, and the shooting angles of different videos to be detected in the videos to be detected are different;
and the format conversion module is used for respectively carrying out format conversion on the plurality of videos to be detected and generating the plurality of images to be detected.
According to the application, the images to be detected are stored in the first server, in the process that n target images to be detected in the images to be detected need to be detected, n target images to be detected are downloaded through n pieces of address information, and then the n target images to be detected are detected, so that the situation that a large number of pictures occupy space is avoided, and the efficiency of image detection is improved.
The embodiment of the application also provides electronic equipment. Referring to fig. 3, an electronic device may include a processor 301, a memory 302, and a program 3021 stored on the memory 302 and executable on the processor 301.
Program 3021, when executed by processor 301, may implement any of the steps in the method embodiment corresponding to fig. 1:
storing a plurality of images to be detected in a first server to generate a plurality of address information, wherein the images to be detected correspond to the address information one by one;
Generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the plurality of images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and the n is a positive integer;
Acquiring the n target to-be-detected images in a first server based on the address queue, performing image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of target objects included in the corresponding target to-be-detected images.
Optionally, the obtaining the n target images to be detected in the first server based on the address queue, and performing image detection on the n target images to be detected, to generate n detection results, includes:
Acquiring the n target images to be detected from the first server based on the address queue;
and sequentially inputting the n target images to be detected into a target image detection model for image detection, and generating the n detection results, wherein the target image detection model is used for detecting the number of the target objects in the input images.
Optionally, the n target images to be detected are sequentially input into a target image detection model to perform image detection, and before the n detection results are generated, the method further includes:
Acquiring a training data set, wherein the training data set comprises a plurality of training data, each training data comprises a sample image and label information corresponding to the sample image, the plurality of label information corresponds to the plurality of sample images one by one, the sample images comprise at least one image of the target object, and the label information is used for indicating the number of the target objects included in the corresponding sample images;
and training the image detection model by using the training data set to obtain the target image detection model.
Optionally, the sequentially inputting the n target images to be detected into a target image detection model for image detection, generating the n detection results includes:
Obtaining m target image detection models, wherein m is a positive integer;
And distributing the n target images to be detected to the m target image detection models according to a preset rule for image detection, and generating the n detection results.
Optionally, the method further includes, after acquiring the n target images to be detected in the first server based on the address queue and performing image detection on the n target images to be detected, generating n detection results:
storing the n detection results in a second server;
receiving a data query request sent by the user side, wherein the data query request is used for querying the n detection results;
Downloading target data in the second server based on the data query request, wherein the target data comprises the n detection results;
and sending the target data to the user terminal.
Optionally, before storing the plurality of images to be detected in the first server and generating the plurality of address information, the method further includes:
Acquiring a plurality of videos to be detected, wherein the videos to be detected are videos shot for a target area, and the shooting angles of different videos to be detected in the videos to be detected are different;
and respectively carrying out format conversion on the plurality of videos to be detected to generate the plurality of images to be detected.
According to the application, the images to be detected are stored in the first server, in the process that n target images to be detected in the images to be detected need to be detected, n target images to be detected are downloaded through n pieces of address information, and then the n target images to be detected are detected, so that the situation that a large number of pictures occupy space is avoided, and the efficiency of image detection is improved.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the processes of the above-mentioned image detection method embodiment, and can achieve the same technical effects, so that repetition is avoided, and no further description is given here. The computer readable storage medium is, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk or an optical disk.
The embodiment of the present application further provides a computer program product, which is stored in a storage medium, and the computer program product is executed by at least one processor to implement the processes of the above-mentioned image detection method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and no redundant description is provided herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are to be protected by the present application.
Claims (10)
1. An image detection method, the method comprising:
storing a plurality of images to be detected in a first server to generate a plurality of address information, wherein the images to be detected correspond to the address information one by one;
Generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the plurality of images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and the n is a positive integer;
Acquiring the n target to-be-detected images in a first server based on the address queue, performing image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of target objects included in the corresponding target to-be-detected images.
2. The method according to claim 1, wherein the obtaining the n target to-be-detected images in the first server based on the address queue, and performing image detection on the n target to-be-detected images, generates n detection results, includes:
Acquiring the n target images to be detected from the first server based on the address queue;
and sequentially inputting the n target images to be detected into a target image detection model for image detection, and generating the n detection results, wherein the target image detection model is used for detecting the number of the target objects in the input images.
3. The method according to claim 2, wherein the sequentially inputting the n target images to be detected into a target image detection model for image detection, and before generating the n detection results, the method further comprises:
Acquiring a training data set, wherein the training data set comprises a plurality of training data, each training data comprises a sample image and label information corresponding to the sample image, the plurality of label information corresponds to the plurality of sample images one by one, the sample images comprise at least one image of the target object, and the label information is used for indicating the number of the target objects included in the corresponding sample images;
and training the image detection model by using the training data set to obtain the target image detection model.
4. The method according to claim 3, wherein sequentially inputting the n target images to be detected into a target image detection model for image detection, generating the n detection results includes:
Obtaining m target image detection models, wherein m is a positive integer;
And distributing the n target images to be detected to the m target image detection models according to a preset rule for image detection, and generating the n detection results.
5. The method according to claim 1, wherein the acquiring the n target to-be-detected images in the first server based on the address queue, and performing image detection on the n target to-be-detected images, and after generating n detection results, the method further includes:
storing the n detection results in a second server;
receiving a data query request sent by the user side, wherein the data query request is used for querying the n detection results;
Downloading target data in the second server based on the data query request, wherein the target data comprises the n detection results;
and sending the target data to the user terminal.
6. The method of claim 1, wherein the storing the plurality of images to be detected in the first server, prior to generating the plurality of address information, further comprises:
Acquiring a plurality of videos to be detected, wherein the videos to be detected are videos shot for a target area, and the shooting angles of different videos to be detected in the videos to be detected are different;
and respectively carrying out format conversion on the plurality of videos to be detected to generate the plurality of images to be detected.
7. An image detection apparatus, the apparatus comprising:
The storage module is used for storing a plurality of images to be detected in the first server and generating a plurality of address information, and the images to be detected are in one-to-one correspondence with the address information;
The generating module is used for generating an address queue according to a processing request sent by a user side, wherein the processing request comprises identification information of n target images to be detected, the n target images to be detected are n images to be detected in the plurality of images to be detected, the address queue comprises address information corresponding to the n target images to be detected, and n is a positive integer;
The acquisition module is used for acquiring the n target to-be-detected images in the first server based on the address queue, carrying out image detection on the n target to-be-detected images, and generating n detection results, wherein the n detection results are in one-to-one correspondence with the n target to-be-detected images, and the detection results are used for indicating the number information of the target objects included in the corresponding target to-be-detected images.
8. An electronic device, comprising: a memory, a processor, and a program stored on the memory and executable on the processor; the image detection method according to any one of claims 1 to 6, characterized in that the processor is configured to read a program in a memory to implement the steps in the image detection method.
9. A readable storage medium storing a program, wherein the program when executed by a processor implements the steps in the image detection method according to any one of claims 1 to 6.
10. A computer program product, characterized in that it is stored in a storage medium, which is executed by at least one processor to implement the steps in the image detection method according to any of claims 1 to 6.
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