CN113569813A - Intelligent image recognition system and method based on server side - Google Patents
Intelligent image recognition system and method based on server side Download PDFInfo
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Abstract
The invention discloses an intelligent image identification system and an identification method based on a server side, wherein the system comprises an image snapshot module, an intelligent algorithm module and a result output module which are sequentially and electrically connected together, the image snapshot module is electrically connected with a video server on which a plurality of cameras are hung, the result output module is electrically connected with other systems, and an algorithm scheduling module is electrically connected with the image snapshot module, the intelligent algorithm module, the result output module and a storage module. The system disclosed by the invention is an integrated system based on server-side image snapshot, image intelligent identification and result message output, and improves the adaptability of engineering application. The system is more flexible and practical and has high accuracy of image recognition results.
Description
Technical Field
The invention belongs to the field of image recognition, and particularly relates to an intelligent image recognition system and method based on a server side in the field.
Background
The application field of image recognition is very wide, and the manual recognition method depends on manual staring at the screen, which causes manpower fatigue and has low accuracy. Although the special camera with the image recognition algorithm is provided in the fields of traffic, city management, water conservancy, environmental protection and the like, due to the limitation of the hardware resources of the camera and the constraint of the application scene, the camera with the built-in different algorithms is often required to be installed aiming at different scenes, the image recognition scene is single, the application scene cannot be switched, and therefore the defects that the camera is large in installation variety, large in resources such as network bandwidth occupied, high in hardware cost, incapable of switching the application scene algorithm and the like are caused. Although some image recognition algorithms based on the server side are also available, a complete system is not formed by capturing, processing and result output of a common camera, a scene switching function is not provided, and the efficiency is not high.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent image recognition system and method based on a server side.
The invention adopts the following technical scheme:
the improvement of an intelligent image recognition system based on a server side is that: the intelligent video camera comprises an image snapshot module, an intelligent algorithm module and a result output module which are sequentially and electrically connected together, wherein the image snapshot module is electrically connected with a video server on which a plurality of cameras are hung, the result output module is electrically connected with other systems, and an algorithm scheduling module is electrically connected with the image snapshot module, the intelligent algorithm module, the result output module and a storage module.
Furthermore, the image snapshot module is connected with the video server through the SDK and is responsible for performing timing camera action adjustment and image snapshot on the corresponding camera according to the scene parameters configured by the algorithm scheduling module, and the snapshot image is used by a subsequent intelligent algorithm module.
Further, the algorithm scheduling module performs unified configuration scene parameter management on the control information, the snapshot information, the identification algorithm parameters and the result output parameters of the camera, and stores the scene parameter management in the storage module.
Further, the control information of the camera comprises a camera type, a camera name, an IP address, a port number, a login user name, a password, a camera focal length and a rotation angle; the snapshot information comprises a timing time interval, the number of the snapshot images, an image storage path and a file naming rule; identifying algorithm parameters including scene type, algorithm threshold, identification time range and abnormal elimination parameters; the result output parameters comprise the release message type, the subject name, the image recognition result file path name and the information release mode of the MQ, or the external message release is carried out through the UDP, Kafka, RabbitMQ and RocktetMQ modes.
Furthermore, the algorithm scheduling module adopts a multithreading processing architecture, after the image is captured, a thread is started according to each algorithm module, and the intelligent algorithm module is called to carry out image recognition algorithm calculation processing.
Furthermore, the intelligent algorithm module adopts an opencv library for secondary development.
Further, the storage module is a database of MySQL, Oracle, PostgreSQL or SqlServer, or an XML file.
The improvement of the intelligent image recognition method based on the server side by using the system is that the intelligent image recognition method based on the server side comprises the following steps:
step 5, after the intelligent algorithm module finishes processing, the algorithm scheduling module schedules a result output module to externally release the generated identification result information;
and 7, finishing and exiting the system after no scene exists.
Further, step 4 specifically includes: reading an image file, and carrying out image identification processing according to an algorithm scheduling module method and parameters, wherein the image identification processing comprises the following steps: graying, noise reduction filtering, edge calculation or difference calculation, data filtering, invalid recognition result elimination, final calculation result comparison with a preset threshold value, and message issuing and result picture storage when the final calculation result exceeds the threshold value.
Further, step 5 specifically includes: and the result output module identifies the image exceeding the threshold, utilizes the message queue ActiveMQ to issue messages externally, and renames and stores the result picture for other systems to read the image identification result and manually refer and recheck the image.
The invention has the beneficial effects that:
the system disclosed by the invention is an integrated system based on server-side image snapshot, image intelligent identification and result message output, and improves the adaptability of engineering application. The system is more flexible and practical and has high accuracy of image recognition results. By utilizing the opencv library and adopting a thread pool technology, multi-camera and multi-scene multi-thread simultaneous processing is realized, and the utilization efficiency of the server and the timeliness of image identification are effectively improved.
The method disclosed by the invention can be implemented by adopting pure software, hardware is not increased, and the cost can be effectively reduced; the deployment process is simple, the application is flexible, the maintenance is convenient, and the operation is reliable; and flexible scene parameter configuration and condition filtering are adopted, so that the accuracy of image identification is high.
Drawings
FIG. 1 is a schematic diagram of the system disclosed in example 1 of the present invention;
FIG. 2 is a schematic flow chart illustrating the operation of the method disclosed in example 1 of the present invention;
FIG. 3 is a schematic flow chart of step 4 of the method disclosed in embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment 1 discloses an intelligent image recognition system based on a server, which realizes image capturing and intelligent recognition functions of a common camera by being deployed on the server. As shown in fig. 1, the system comprises an image capturing module 1, an intelligent algorithm module 2 and a result output module 3 which are electrically connected together in sequence, wherein the image capturing module is electrically connected with a video server 5 on which a plurality of cameras 4 are hung, the result output module is electrically connected with other systems 6, and an algorithm scheduling module 7 is electrically connected with the image capturing module 1, the intelligent algorithm module 2, the result output module 3 and a storage module 8. The image capturing module is used for capturing images from a video stream or a video server at regular time; the algorithm scheduling module is responsible for scheduling the intelligent algorithm module; the intelligent algorithm module is responsible for carrying out real-time image recognition on the image; and the result output module is responsible for externally releasing the image identification result information for other systems to use.
In a traditional video monitoring system, through the matched operation of an algorithm scheduling module, an image snapshot module, an intelligent algorithm module and a result output module, the automatic snapshot of images, intelligent image recognition and recognition result pushing of a plurality of cameras and various scenes are realized, the resource utilization rate of a server system is effectively improved, and the timeliness and accuracy of image recognition are improved.
In this embodiment, the image capture module establishes a connection with the video server through the SDK (an abbreviation of Software Development Kit, which is a generic name of Software Development kits such as a certain system Software interface, a code, an example, and the like), and is responsible for performing timing camera action adjustment and image capture on a corresponding camera according to scene parameters configured by the algorithm scheduling module, and the captured image is used by a subsequent intelligent algorithm module.
In order to realize image identification application of different cameras and different scenes, the algorithm scheduling module carries out unified image identification scene configuration management on control information, snapshot information, identification algorithm parameters and result output parameters of the cameras and stores the unified image identification scene configuration management into the storage module, so that a user is allowed to edit, add, delete and the like the scenes, and the flexibility and the practicability of scene image identification are improved.
The control information of the camera comprises camera action parameters such as camera type, camera name, IP address, port number, login user name, password, camera focal length, rotation angle (if any) and the like; the snapshot information comprises parameters such as a timing time interval, the number of the snapshot images, an image storage path, a file naming rule and the like; the identification algorithm parameters comprise scene type, algorithm threshold, identification time range, rejection abnormal parameters and other parameters related to image identification; the result output parameters comprise parameter configurations of the MQ, such as the publishing message type, the subject name, the image recognition result file path name, the information publishing mode and the like. The result output module in the invention adopts an ActiveMQ mode, and can also issue external messages in UDP, Kafka, RabbitMQ and RocktetMQ modes, without influencing the application effects of intelligent image identification and information issue.
Through scene parameter configuration management, the intelligent image recognition system of the server side can be suitable for different cameras under different scenes, corresponding snapshot can be carried out according to the scenes, scene parameters are called to carry out image intelligent recognition, invalid image recognition results are eliminated, recognition results are issued according to a preset mode, and therefore the image recognition system which is flexible in configuration, runs automatically and recognizes intelligently is formed.
The algorithm scheduling module adopts a multithread processing architecture, starts a thread according to each algorithm module after the image is captured, and calls the intelligent algorithm module to perform image recognition algorithm calculation processing.
And the intelligent algorithm module adopts an opencv library for secondary development. Opencv is a BSD license (open source) based distributed cross-platform computer vision and machine learning software library that can run on Linux, Windows, Android, and Mac OS operating systems.
The storage module accesses scene configuration data by adopting MySQL, Oracle, PostgreSQL or SqlServer databases or manages by adopting XML file access.
The embodiment also discloses an intelligent image recognition method based on a server, and the system comprises the following steps as shown in fig. 2:
step 5, after the intelligent algorithm module finishes processing, the algorithm scheduling module schedules a result output module to externally release the generated identification result information;
and 7, finishing and exiting the system after no scene exists.
As shown in fig. 3 (flowchart of edge extraction algorithm), step 4 specifically includes: reading image files, and carrying out image identification processing according to an algorithm scheduling module method and parameters, wherein the image identification processing method comprises the following steps: graying, noise reduction filtering, edge calculation or difference calculation and the like, the actual situation of a scene is fully considered in an algorithm, the sensitivity and the accuracy are considered, for example, a vehicle is identified, multiple conditions such as area, shape, image area, time range and the like are comprehensively considered for data filtering, invalid identification results are removed, the final calculation result is compared with a preset threshold value, and message issuing and result picture storage are carried out when the calculation result exceeds the threshold value.
The step 5 specifically comprises the following steps: the result output module identifies the image exceeding the threshold, and issues the Message to the outside by using an ActiveMQ (Message Queue) to solve the problems of application decoupling, asynchronous Message, traffic peak clipping and the like, so as to realize high-performance, high-availability, scalability and final consistency architecture, and rename and store the result picture for other systems to read the image identification result and manually refer to and recheck the image.
Claims (10)
1. The utility model provides an intelligent image recognition system based on server side which characterized in that: the intelligent video camera comprises an image snapshot module, an intelligent algorithm module and a result output module which are sequentially and electrically connected together, wherein the image snapshot module is electrically connected with a video server on which a plurality of cameras are hung, the result output module is electrically connected with other systems, and an algorithm scheduling module is electrically connected with the image snapshot module, the intelligent algorithm module, the result output module and a storage module.
2. The intelligent image recognition system based on the server side according to claim 1, wherein: the image snapshot module is connected with the video server through the SDK and is responsible for performing timing camera action adjustment and snapshot images on the corresponding cameras according to the scene parameters configured by the algorithm scheduling module, and the snapshot images are used by the subsequent intelligent algorithm module.
3. The intelligent image recognition system based on the server side according to claim 1, wherein: and the algorithm scheduling module performs unified configuration scene parameter management on the control information, the snapshot information, the identification algorithm parameters and the result output parameters of the camera and stores the scene parameter management in the storage module.
4. The intelligent image recognition system based on the server side according to claim 3, wherein: the control information of the camera comprises the type of the camera, the name of the camera, an IP address, a port number, a login user name, a password, the focal length of the camera and a rotation angle; the snapshot information comprises a timing time interval, the number of the snapshot images, an image storage path and a file naming rule; identifying algorithm parameters including scene type, algorithm threshold, identification time range and abnormal elimination parameters; the result output parameters comprise the release message type, the subject name, the image recognition result file path name and the information release mode of the MQ, or the external message release is carried out through the UDP, Kafka, RabbitMQ and RocktetMQ modes.
5. The intelligent image recognition system based on the server side according to claim 1, wherein: the algorithm scheduling module adopts a multithread processing architecture, starts a thread according to each algorithm module after the image is captured, and calls the intelligent algorithm module to perform image recognition algorithm calculation processing.
6. The intelligent image recognition system based on the server side according to claim 1, wherein: and the intelligent algorithm module adopts an opencv library for secondary development.
7. The intelligent image recognition system based on the server side according to claim 1, wherein: the storage module is MySQL, Oracle, PostgreSQL or SqlServer database, or XML file.
8. A server-based intelligent image recognition method, using the system of claim 1, comprising the steps of:
step 1, initializing a system, and connecting an algorithm scheduling module with a storage module to load all scene parameters;
step 2, the algorithm scheduling module starts an image snapshot module at regular time according to a certain scene and is connected with a corresponding camera through the SDK;
step 3, the image snapshot module carries out image snapshot according to preset scene parameters;
step 4, the algorithm scheduling module schedules an intelligent algorithm module according to the scene parameters, carries out image intelligent identification and eliminates abnormal results, and records an algorithm running log in the middle;
step 5, after the intelligent algorithm module finishes processing, the algorithm scheduling module schedules a result output module to externally release the generated identification result information;
step 6, the algorithm scheduling module executes the next scene and circularly executes the steps 2 to 6;
and 7, finishing and exiting the system after no scene exists.
9. The server-side-based intelligent image recognition method according to claim 8, wherein the step 4 specifically comprises: reading an image file, and carrying out image identification processing according to an algorithm scheduling module method and parameters, wherein the image identification processing comprises the following steps: graying, noise reduction filtering, edge calculation or difference calculation, data filtering, invalid recognition result elimination, final calculation result comparison with a preset threshold value, and message issuing and result picture storage when the final calculation result exceeds the threshold value.
10. The server-side-based intelligent image recognition method according to claim 8, wherein the step 5 specifically comprises: and the result output module identifies the image exceeding the threshold, utilizes the message queue ActiveMQ to issue messages externally, and renames and stores the result picture for other systems to read the image identification result and manually refer and recheck the image.
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