Drum feature positioning method and device for drum labeling machine

Abstract

The present invention discloses a method and device for locating barrel features in a barrel labeling machine. The method comprises acquiring video stream data of the barrel using a pre-set plurality of cameras, performing feature recognition on the video stream data using a pre-trained barrel feature recognition network, and saving the recognized target features and the camera numbers corresponding to the target features in a buffer file; initializing the buffer file; after clearing the buffer file, waiting for a new piece of recognition data in the buffer file, inputting the recognition data in the buffer file into a pre-established barrel position angle model to obtain the current angular position information of the target feature; generating status information based on the current angular position information of the barrel target feature; transmitting the angular position information and the status information to a server, which then moves the barrel target feature to a specified position based on the received angular position information and status information. The present invention solves the problem of slow positioning speed in conventional labeling machines.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence, and in particular relates to a method and device for locating drum features for a drum labeling machine. Background Art

[0002] Drum labeling machines are widely used in industrial production processes, primarily for precisely applying labels to drum surfaces. Their applications span a wide range of industries, including food, beverages, chemicals, and pharmaceuticals. In these industries, drums typically hold a variety of liquid, solid, or powdered substances. Therefore, ensuring the correct positioning and application of labels on the drum surface is crucial for product aesthetics, brand recognition, and communication.

[0003] Accurately locating the position and orientation of drums is a key challenge in traditional labeling processes. Drums vary in geometry and size, and can have slight deviations due to manufacturing tolerances. Therefore, ensuring the precise fit of the label to the drum is crucial for maintaining product quality and aesthetics.

[0004] Existing labeling technologies typically rely on mechanical positioning devices or optical sensors to detect and adjust the position of drums. However, these methods can encounter challenges when dealing with drums of varying sizes, shapes, and materials. For example, mechanical positioning devices may require adjustment for drums of varying sizes, while optical sensors can misjudge reflective or transparent materials.

[0005] Therefore, developing a universal and efficient method for locating drum features is crucial for ensuring stable operation of labeling machines and adapting to diverse production needs. This method should be able to reliably locate drums of varying types, sizes, and materials, ensuring accurate label placement. This not only improves production efficiency but also reduces human intervention and errors, ultimately enhancing production line automation and product quality. Summary of the Invention

[0006] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a method and device for positioning drum features for a drum labeling machine, so as to at least solve the problems of versatility and low positioning efficiency in the traditional labeling process in the related art.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a method for locating features of a drum for a drum labeling machine, comprising the following steps:

[0009] Acquire video stream data of the barrel based on a plurality of preset cameras, perform feature recognition on the video stream data using a pre-trained barrel feature recognition network, and save the recognized target features and the camera numbers corresponding to the target features into a buffer file;

[0010] Initialize the buffer file. When the execution signal from the server is monitored, the command task is executed according to the execution signal and the current buffer file is cleared.

[0011] After clearing the buffer file, wait for a new piece of recognition data in the buffer file, input the recognition data in the buffer file into the pre-established barrel position angle model, and obtain the current angle position information of the target feature;

[0012] Generate state information based on the current angular position information of the barrel target feature, wherein the state information is used to indicate that the angular position information is the latest;

[0013] The angle position information and the state information are transmitted to the server side, and the server side moves the target feature of the barrel to the designated position according to the received angle position information and state information.

[0014] As a preferred technical solution, the video stream data of the barrel is obtained based on the preset multiple cameras, specifically:

[0015] A camera is set up in four directions on the side of the barrel, and the video stream data of the side of the barrel is obtained by using the camera. Each camera covers a 90° range of the side of the barrel.

[0016] As a preferred technical solution, the pre-trained barrel feature recognition network is used to perform feature recognition on the video stream data, and the recognized target features and the camera numbers corresponding to the target features are saved in a buffer file, specifically:

[0017] The target feature identified by the barrel feature recognition network is represented by a rectangular bounding box. The target feature information of the barrel is identified in the acquired video stream data. The bounding box is marked with the target category information and target confidence corresponding to the identified target feature;

[0018] After identifying the bounding box corresponding to the target feature, according to the target category information and target confidence of the target, select the target whose target category information is the set category and the target confidence meets the set threshold, then use the target as the barrel target feature, and the bounding box corresponding to the target is the bounding box corresponding to the described barrel target feature;

[0019] The camera number of the video stream data from which the target feature of the identified barrel originates and the x-coordinate of the center coordinate of the bounding box are set as the identified feature information, which is the target feature and the camera number corresponding to the target feature.

[0020] As a preferred technical solution, the clearing of the buffer file and waiting for a piece of identification data in the buffer file is specifically as follows:

[0021] First, wait for the execution signal from the server and listen for the execution signal from the specified register on the server;

[0022] After waiting for the execution signal given by the designated register on the server side, clear the buffer file storing the recognized barrel feature identification information, wherein the barrel feature identification information is the target feature and the camera number corresponding to the target feature;

[0023] Finally, wait for the latest barrel feature recognition information to be saved in the buffer file.

[0024] As a preferred technical solution, the barrel position angle model is a model trained and fitted based on random forest regression, specifically:

[0025] After obtaining the latest recognized barrel feature information in the buffer file, the camera number and the x-coordinate of the center coordinate of the bounding box in the feature information are input into the barrel position angle model to obtain the current angle position information of the barrel target feature;

[0026] The barrel position angle model outputs position angle information, and the position angle information value is between 0° and 360°. The camera number of the identified barrel feature information and the x-coordinate of the center coordinate of the bounding box are input into the barrel feature position angle model, and regression analysis is performed based on multiple decision trees. The multiple decision trees are constructed by randomly selecting subsets and features in the data set, and finally a predicted position angle information is output.

[0027] As a preferred technical solution, the recognition data in the buffer file is input into the pre-established barrel position angle model to obtain the current angular position information of the target feature, specifically:

[0028] Initialize the position information, status information, waiting signal and real angle information of the buffer file;

[0029] A plurality of identification data are input into the barrel position angle model to obtain a plurality of angle position information, and an average value of the plurality of angle position information is taken as the output of the barrel position angle model.

[0030] As a preferred technical solution, after transmitting the position information and the status information to the server, it is first determined whether the transmitted angular position information is the latest. If it is the latest, the server will transfer the target feature of the barrel to the specified position; after the operation is completed, wait for the next barrel to arrive at the specified position.

[0031] In a second aspect, the present invention provides a barrel feature positioning system for a barrel labeling machine, comprising a target feature acquisition module, an initialization module, an angle position information acquisition module, a status information acquisition module, and a server execution module;

[0032] The target feature acquisition module is used to acquire video stream data of the barrel based on a plurality of preset cameras, perform feature recognition on the video stream data using a pre-trained barrel feature recognition network, and save the recognized target features and the camera numbers corresponding to the target features into a buffer file;

[0033] The initialization module is used to initialize the buffer file. When the execution signal of the server is monitored, the instruction task is executed according to the execution signal and the current buffer file is cleared.

[0034] The angle position information acquisition module is used to clear the buffer file, wait for a new piece of identification data in the buffer file, input the identification data in the buffer file into the pre-established barrel position angle model, and obtain the current angle position information of the target feature;

[0035] The state information acquisition module is used to generate state information based on the current angular position information of the barrel target feature, and the state information is used to indicate that the angular position information is the latest;

[0036] The server execution module is used to transmit the angle position information and the status information to the server side, and the server side moves the target feature of the barrel to a specified position according to the received angle position information and status information.

[0037] In a third aspect, the present invention provides an electronic device, comprising:

[0038] at least one processor; and,

[0039] a memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores computer program instructions that can be executed by the at least one processor. The computer program instructions are executed by the at least one processor to enable the at least one processor to perform the drum feature positioning method for a drum labeling machine.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the drum feature positioning method for a drum labeling machine.

[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0043] The present invention provides a universal method for locating barrel features in a barrel labeling machine. First, the system utilizes advanced neural network algorithms to achieve higher-precision barrel feature location, ensuring precise label application and improving labeling accuracy and consistency. Second, by utilizing a pre-trained barrel feature recognition network, the system can rapidly identify barrel features and rapidly generate location information through a real-time data processing module, accelerating the entire labeling process and improving production efficiency. Furthermore, the system employs a universal method that is applicable to barrels of varying shapes and sizes and exhibits a certain degree of fault tolerance, making it widely applicable across a wide range of industrial sectors. Most importantly, controlled by a server-side operation module, the entire labeling process is highly automated, simplifying the operational flow, reducing the likelihood of manual intervention and operational errors, improving the stability and reliability of the production line, and reducing production costs. In summary, the barrel feature location method and universal system of the present invention offer significant advantages in improving labeling accuracy, accelerating processing speed, enhancing versatility, and simplifying operational procedures. They are expected to play a significant role in industrial production and promote the automation and intelligentization of production. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is the barrel feature positioning method for a barrel labeling machine according to Example 1 of the present application.

[0046] Figure 2 This is a schematic diagram of the camera and barrel position structure in Examples 1, 2, 3, and 4.

[0047] Figure 3 This is the workflow diagram of step S1 of Example 1 of the present application.

[0048] Figure 4 This is a workflow diagram of the barrel position angle model in step S3 of Example 1 of the present application.

[0049] Figure 5This is a workflow diagram of the server side in step S5 of Example 1 of the present application.

[0050] Figure 6 Example 2 of the present application provides a workflow diagram for deploying a barrel feature positioning method for a barrel labeling machine to an industrial assembly line.

[0051] Figure 7 Embodiment 3 of the present application provides a barrel feature positioning system for a barrel labeling machine.

[0052] Figure 8 Embodiment 4 of the present application provides an electronic device for implementing a barrel feature positioning method for a barrel labeling machine. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0054] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0055] Object detection algorithms are a key technology in the field of computer vision, designed to identify and locate objects in images or videos. These algorithms typically consist of two stages: first, a feature extraction network (such as a convolutional neural network) extracts feature representations from the input image; then, a classifier or regressor identifies the category or bounding box of each region of interest. Currently, popular object detection algorithms include region-based methods (such as R-CNN, Fast R-CNN, and Faster R-CNN), single-stage methods (such as YOLO and SSD), and hybrid algorithms that combine the two approaches. These algorithms are widely used in fields such as image recognition, intelligent surveillance, and autonomous driving, making them one of the most important algorithms in computer vision that has garnered significant attention.

[0056] DeepStream is a high-performance real-time data processing platform launched by NVIDIA. Combining GPU-accelerated computing, deep learning model support, and real-time stream processing technology, it provides users with powerful tools and libraries to accelerate the development and deployment of applications such as video analysis and object detection. Its flexible deployment options and easy-to-use development interface enable developers to quickly build high-performance, low-latency real-time data processing applications.

[0057] The random forest regression algorithm is a machine learning method based on an ensemble of decision trees. It performs regression predictions by constructing multiple decision trees and taking the average or voting results. When constructing each decision tree, random forests employ random feature selection and bootstrap sampling techniques to increase model diversity and generalization capabilities. This enables random forests to effectively handle high-dimensional data and large datasets, resulting in strong robustness and predictive performance. It is widely used in fields such as finance and healthcare to solve regression problems.

[0058] Example 1

[0059] like Figure 1 As shown, in this embodiment 1, a method for locating features of a barrel for a barrel labeling machine includes the following steps:

[0060] Step S1: Feature recognition.

[0061] Specifically, the video stream data of the barrel is obtained based on a plurality of preset cameras, the feature recognition of the video stream data is performed using a pre-trained barrel feature recognition network, and the recognized target features and the camera numbers corresponding to the target features are saved in a buffer file;

[0062] In this embodiment, if Figure 2 As shown in the figure, a camera is set up in each of the four directions on the side of the barrel. Each camera captures video stream data from the barrel's side, with each camera covering a 90° angle. Specifically, four cameras at each of the four directions capture a frame of video stream data from the barrel. Feature recognition is performed on the captured stream data, and the generated barrel feature information and the corresponding camera number are saved in a buffer file.

[0063] Further, such as Figure 3 As shown, step S1 is specifically as follows:

[0064] Step S101: Turn on the camera.

[0065] In this embodiment, before executing the embodiment of the present application, Figure 2 As shown, you need to turn on the four cameras in the four directions on the side of the barrel first. Each camera covers 90° of the side of the barrel. The four cameras can completely cover the entire side of the barrel.

[0066] Step S102: Read video stream data through the camera.

[0067] In this embodiment, the Deepstream tool is used to read the real-time video streams of four cameras, and the barrel target features are recognized frame by frame on the video stream data read by the four cameras.

[0068] Step S103: Identify the video stream data.

[0069] In this embodiment, a pre-trained barrel feature recognition network is used to detect the characteristic position of the barrel in the side images of the barrel acquired by four cameras, and to determine the camera corresponding to the target feature recognition, wherein the pre-trained barrel feature recognition network is a neural network trained based on a target detection algorithm.

[0070] Step S104: Identify the characteristic information of the barrel.

[0071] In this embodiment, the target feature identified by the barrel feature recognition network is represented by a rectangular bounding box, and the target feature information of the barrel is identified in the video stream data obtained by the four cameras. The bounding box marks the target category information and target confidence corresponding to the identified barrel target feature. After identifying the bounding box corresponding to the target feature of the barrel in the image, according to the target category information and target confidence of the target, the target whose target category information is the set category and the target confidence meets the set threshold is selected, and the target is used as the barrel target feature, and the bounding box corresponding to the target is the bounding box corresponding to the described barrel target feature; the camera number of the video stream data from which the identified barrel target feature originates and the x-coordinate of the center coordinate of the bounding box are set as the identified feature information.

[0072] Step S105: Save the identified barrel feature information to the buffer file; return to step S102

[0073] In this embodiment, the recognized barrel feature information and the corresponding camera number are saved in a buffer file in real time.

[0074] S2. Initialize the buffer file. When the execution signal from the server is monitored, the instruction task is executed according to the execution signal and the current buffer file is cleared.

[0075] Step S2 specifically includes:

[0076] Step S201: Connect to the server, and connect the industrial computer to the server.

[0077] In this embodiment, before executing the positioning method of the embodiment of the present application, the industrial computer is first connected to the server side, so that the data processing module and the server side operation module can communicate.

[0078] Step S202: Initialize parameters.

[0079] In this embodiment, before executing the positioning method of the embodiment of the present application, it is necessary to initialize the location information, status information, waiting signal and other parameters of the buffer file.

[0080] Step S203: monitoring the execution signal from the server.

[0081] In this embodiment, before executing the positioning method of the embodiment of the present application, it is necessary to monitor the signal of the register on the server side. The register is the location where the execution signal is stored in the operation module on the server side.

[0082] Step S204: Determine the signal.

[0083] In this embodiment, before executing the embodiment of the present application, it is necessary to first determine the signal of the server-side register. If the signal is 1, proceed to step S205; otherwise, return to step S203.

[0084] Step S205: Clear the buffer file.

[0085] In this embodiment, before executing the embodiment of the present application, it is necessary to first clear the feature information and camera number of the barrel stored in the buffer file by the feature recognition module.

[0086] After clearing the buffer file, wait for a new piece of identification data in the buffer file (i.e., the target feature and the camera number corresponding to the target feature), input the identification data in the buffer file into the pre-established barrel position angle model, and obtain the current angle position information of the target feature;

[0087] S3, after clearing the buffer file, waiting for a new piece of identification data in the buffer file (i.e., the target feature and the camera number corresponding to the target feature), inputting the identification data in the buffer file into the pre-established barrel position angle model to obtain the current angle position information of the target feature;

[0088] Furthermore, the barrel position angle model is a model trained and fitted based on random forest regression, specifically:

[0089] After obtaining the latest recognized barrel feature information in the buffer file, the camera number and the x-coordinate of the center coordinate of the bounding box in the feature information are input into the barrel position angle model to obtain the current angle position information of the barrel target feature;

[0090] The barrel position angle model outputs position angle information, and the position angle information value is between 0° and 360°. The camera number of the identified barrel feature information and the x-coordinate of the center coordinate of the bounding box are input into the barrel feature position angle model, and regression analysis is performed based on multiple decision trees. The multiple decision trees are constructed by randomly selecting subsets and features in the data set, and finally a predicted position angle information is output.

[0091] In one embodiment, Figure 4 As shown, the workflow of the barrel position angle model is as follows:

[0092] Step S301: Start the feature recognition module.

[0093] In this embodiment, if Figure 2 As shown, four cameras in four directions of the barrel respectively obtain one frame of stream data of the barrel, perform feature recognition on the obtained stream data, and save the barrel feature information and camera number generated by the recognition into a buffer file.

[0094] Step S302: Initialize the barrel position.

[0095] In this embodiment, the position of the target feature of the barrel is first initialized to the position of 0°.

[0096] Step S303: suck up the barrel.

[0097] In this embodiment, sucking up the barrel can increase the accuracy of the feature recognition module in identifying the barrel features, which is indispensable for preparing the data set.

[0098] Step S304: Initialize parameters.

[0099] In this embodiment, before executing the positioning method of the embodiment of the present application, it is necessary to initialize parameters such as the position, status information, waiting signal, and true angle information of the buffer file.

[0100] Step S305: Determine the accumulated rotation angle.

[0101] In this embodiment, the cumulative rotation angle of the barrel feature is determined. If the cumulative rotation angle of the barrel feature is less than 360°, the process goes to step S306 and sends an execution signal 1 to the server. If the cumulative rotation angle of the barrel feature is greater than or equal to 360°, the data set is created.

[0102] Step S306: Send execution signal 1 to the server.

[0103] In this embodiment, it is necessary to automatically collect and create data sets and use existing modules, so the data processing module needs to set the register execution signal of the server side to 1 first.

[0104] Step S307: monitor the execution signal from the server.

[0105] In this embodiment, the data processing module needs to monitor the server-side register execution signal.

[0106] Step S308: Determine the signal.

[0107] In this embodiment, the data processing module determines the execution signal of the monitored server side. If the execution signal is 1, the process goes to step S309 to clear the buffer file.

[0108] Step S309: Clear the buffer file.

[0109] In this embodiment, before reading the characteristic information of the barrel identified by the characteristic identification module (the barrel is already in place), it is necessary to clear the characteristic information stored in the buffer file.

[0110] Step S310: The data set is created.

[0111] Step S311: Waiting for 5 pieces of identification data in the buffer file.

[0112] In the embodiment, the data processing module needs to wait for 5 new barrel feature data to be stored in the buffer file after it is cleared.

[0113] Step S312: Calculate the average value of the barrel feature information.

[0114] In this embodiment, the data processing module needs to extract the latest 5 barrel feature information in the buffer file, calculate the average value of the barrel feature information (x coordinate of the center coordinate of the bounding box), and use the average value as the barrel feature information recognized by the feature recognition module.

[0115] Step S313: Save the camera number, barrel feature information, and the position angle of the current barrel target feature into a data set file.

[0116] In this embodiment, the data processing module saves the camera number, barrel feature information and accumulated angle information (the actual position angle of the barrel feature) into a data set file. Each piece of data in the data set is composed of these three items of data.

[0117] Step S314: Angle increment step.

[0118] In this embodiment, the cumulative angle of the barrel feature needs to add the step size to the cumulative angle to serve as the next round of cumulative angle of the barrel feature.

[0119] Step S315: Send the step angle and status information to the server-side operation module. Return to step S305 to determine the cumulative angle.

[0120] In this embodiment, the data processing module needs to send the step angle and status information to the server-side operation module for controlling the step angle of the barrel target feature rotation and then performing the next round of data collection.

[0121] The process ends.

[0122] S4. generating state information based on the current angular position information of the barrel target feature, wherein the state information is used to indicate that the angular position information is the latest;

[0123] S5. The angle position information and the state information are transmitted to the server side, and the server side moves the target feature of the barrel to a designated position according to the received angle position information and state information.

[0124] In a specific embodiment, Figure 5 As shown, the workflow on the server side is as follows:

[0125] Step S501: Wait for the next barrel to arrive at the designated position.

[0126] In this embodiment, before executing the embodiment of the present application, it is necessary to wait for the barrel to reach the specified position.

[0127] Step S502: Send execution signal 1.

[0128] In this embodiment, after detecting that the barrel has reached the designated position, the server-side operation module sends an execution signal 1 to the data processing module to start processing the data for identifying the target features of the barrel.

[0129] Step S503: Wait for the return of location information and status information.

[0130] In this embodiment, it is necessary to wait for the data processing module to return the direction, angle and status information of the drum that needs to be rotated.

[0131] Step S504: Determine whether the processing time exceeds 10 seconds.

[0132] In this embodiment, it is determined whether the data processing time exceeds 10 seconds. If the data processing time exceeds 10 seconds, go to step S505 and set the execution signal to 0; if the data processing time does not exceed 10 seconds, go to step S506 to determine the status information; whether the processing time exceeds 10 seconds does not affect subsequent operations. When the data processing time exceeds 10 seconds, setting the execution signal to 0 can conveniently control the start of the next process.

[0133] Step S505: Set the execution signal to 0.

[0134] In this embodiment, setting the execution signal to 0 can facilitate the control of the start of the next process.

[0135] Step S506: Determine the status information.

[0136] In this embodiment, the status information sent by the data processing module can be judged to be the latest position information of the barrel sent to the server operation module.

[0137] Step S507: operate the barrel according to the position information.

[0138] In this embodiment, the server-side operation module performs corresponding operations on the barrel according to the barrel position information (the direction and angle that the barrel needs to rotate) transmitted by the data processing module, so that the barrel target feature moves to a specific position.

[0139] Step S508: Set the execution signal to 0.

[0140] In this embodiment, after the corresponding operation is performed on the barrel, the execution signal in the register is set to 0 to facilitate the control of the start of the next process.

[0141] Step S509: Wait for the current barrel operation to be completed. Return to step S501

[0142] In this embodiment, after waiting for the drum to complete its rotation and be put down, and the roller to be conveyed forward, the process returns to step S501 and waits for the next drum to arrive at the designated position, and a new process is performed.

[0143] Example 2

[0144] like Figure 6 As shown, Example 6 of the present application provides a workflow diagram for deploying a barrel feature positioning method for a barrel labeling machine to an industrial assembly line. The process includes the following steps:

[0145] Step S601: Install a fixed camera.

[0146] In this embodiment, four cameras need to be fixed according to the actual assembly line scenario to ensure that all areas on the side of the barrel are covered without blind spots. The following steps depend on the installation settings and camera parameters of these four cameras.

[0147] Step S602: Create a barrel target feature dataset.

[0148] In this embodiment, if Figure 8As shown in the figure, these four cameras are used to collect images of the barrel target features, and as many images as possible are collected to cover all situations of the location of the barrel target features; then annotations are made based on the collected images; each collected image and the annotation information corresponding to the image constitute a data in the data set.

[0149] Step S603: training a barrel target feature recognition model.

[0150] In this embodiment, the pre-trained barrel feature recognition network is a neural network trained based on a target detection algorithm; the barrel target feature data set produced in step S602 is input into the target detection algorithm network for supervised training, and finally a barrel feature recognition model is generated.

[0151] Step S604: Create a barrel feature position angle dataset.

[0152] In this embodiment, a barrel feature position angle dataset is produced. First, the current position angle of the barrel target feature is set to 0°; the suction cup is controlled to suck up the barrel; the barrel feature information is identified, and the camera number, barrel feature information, and the actual current position angle of the barrel target feature are saved in the dataset file; the rotation step angle is 0.1° (the step size is generally set to the accuracy of the turntable); the above operations are repeated until the cumulative rotation angle reaches 360°, and the dataset production is completed.

[0153] Step S605: training and fitting the barrel feature position angle model.

[0154] In this embodiment, the barrel feature position angle model is trained and fitted based on a random forest regression algorithm; the random forest regression algorithm randomly selects the features and data samples of the barrel feature position angle data set produced in step S604 to construct multiple decision tree models to generate a barrel feature position angle model.

[0155] Step S606: Use on an automated production line.

[0156] In this embodiment, after completing the above steps, a general method for positioning barrel features of a barrel labeling machine can be deployed on an automated production line and can be used for all barrel-shaped objects with extractable features on the side.

[0157] Example 3

[0158] See also Figure 7 In another embodiment of the present application, a barrel feature positioning system 100 for a barrel labeling machine is provided, the system comprising a target feature acquisition module 101, an initialization module 102, an angle position information acquisition module 103, a state information acquisition module 104, and a server execution module 105;

[0159] The target feature acquisition module 101 is used to acquire video stream data of the barrel based on a plurality of preset cameras, perform feature recognition on the video stream data using a pre-trained barrel feature recognition network, and save the recognized target features and the camera numbers corresponding to the target features into a buffer file;

[0160] The initialization module 102 is used to initialize the buffer file. When the execution signal from the server is monitored, the instruction task is executed according to the execution signal and the current buffer file is cleared.

[0161] The angle position information acquisition module 103 is used to clear the buffer file, wait for a new piece of identification data (i.e., the target feature and the camera number corresponding to the target feature) to be saved in the buffer file, input the identification data in the buffer file into the pre-established barrel position angle model, and obtain the current angle position information of the target feature;

[0162] The state information acquisition module 104 is used to generate state information based on the current angular position information of the target feature, wherein the state information is used to indicate that the angular position information is the latest;

[0163] The server execution module 105 is used to transmit the angle position information and the status information to the server side, and the server side moves the target feature of the barrel to a specified position according to the received angle position information and status information.

[0164] It will be appreciated that in this embodiment, the target feature acquisition module 101 captures images of the barrel's side at four angles and identifies the barrel's target features in these images, generating barrel feature information and a camera number. The angle position information acquisition module 102 then processes this data and predicts the position angle data of the current barrel's target features. Based on the predicted position angle data of the current barrel's target features, the angle position information acquisition module 102 generates the direction and angle for the barrel to rotate. Furthermore, the angle position information acquisition module 102 generates status information and transmits this direction, angle, and status information to the server execution module 105, which then performs corresponding operations on the barrel based on the received information. When the server execution module 105 completes its operations on the barrel, it sets it down, completing the process.

[0165] In a further embodiment, the target feature acquisition module 101 includes a camera, the angle position information acquisition module 102 is a single chip microcomputer or a computer or a central processing chip, and the server execution module 105 includes a baffle, a suction cup, a roller and a PLC controller;

[0166] The camera is used to collect images of the four angles of the barrel side, and identify the collected images to generate barrel feature information and camera number and transmit them to the angle position information acquisition module 102;

[0167] The baffle is used to assist the next barrel to reach the designated position;

[0168] The suction cup is used to suck up, put down and rotate the drum in a specified direction and angle;

[0169] The roller is used to convey the drum forward;

[0170] The PLC controller is used to receive the information transmitted by the angle position information acquisition module 102 and control the baffle, suction cup and roller to perform corresponding operations according to the information;

[0171] The target feature acquisition module 101 communicates with the angle position information acquisition module 102 via a USB interface; the server-side operation module communicates with the angle position information acquisition module 102 via a network cable interface.

[0172] It should be noted that the barrel feature positioning system for a barrel labeling machine of the present invention corresponds one-to-one to the barrel feature positioning method for a barrel labeling machine of the present invention. The technical features and beneficial effects described in the embodiment of the barrel feature positioning method for a barrel labeling machine are applicable to the embodiment of the barrel feature positioning method for a barrel labeling machine. For specific details, please refer to the description in the embodiment of the method of the present invention, which will not be repeated here. This is hereby stated.

[0173] Furthermore, in the above-described embodiment of the drum feature locating system for a drum labeling machine, the logical division of the various program modules is merely illustrative. In actual applications, the above-described functions may be assigned to different program modules as needed, for example, to meet the configuration requirements of the corresponding hardware or to facilitate software implementation. In other words, the internal structure of the drum feature locating system for a drum labeling machine may be divided into different program modules to perform all or part of the functions described above.

[0174] Example 4

[0175] See also Figure 8 In one embodiment, an electronic device for implementing a barrel feature positioning method for a barrel labeling machine is provided. The electronic device 200 may include a first processor 201, a first memory 202, and a bus. It may also include a computer program stored in the first memory 202 and executable on the first processor 201, such as a barrel feature positioning program 203 for a barrel labeling machine.

[0176] The first memory 202 includes at least one type of readable storage medium, including flash memory, a removable hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the first memory 202 may be an internal storage unit of the electronic device 200, such as a removable hard disk of the electronic device 200. In other embodiments, the first memory 202 may also be an external storage device of the electronic device 200, such as a plug-in removable hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the electronic device 200. Furthermore, the first memory 202 may include both an internal storage unit of the electronic device 200 and an external storage device. The first memory 202 can be used not only to store application software installed on the electronic device 200 and various data, such as the code of the barrel feature location program 203 used in the barrel labeling machine, but also to temporarily store data that has been output or is about to be output.

[0177] In some embodiments, the first processor 201 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The first processor 201 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines, and executing or executing programs or modules stored in the first memory 202, as well as calling data stored in the first memory 202, to perform various functions of the electronic device 200 and process data.

[0178] Figure 8 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 8 The structure shown does not constitute a limitation on the electronic device 200 , and the electronic device 200 may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0179] The barrel feature positioning program 203 for the barrel labeling machine stored in the first memory 202 of the electronic device 200 is a combination of multiple instructions. When executed in the first processor 201, it can achieve the following:

[0180] Acquire video stream data of the barrel based on a plurality of preset cameras, perform feature recognition on the video stream data using a pre-trained barrel feature recognition network, and save the recognized target features and the camera numbers corresponding to the target features into a buffer file;

[0181] Initialize the buffer file. When the execution signal from the server is monitored, the command task is executed according to the execution signal and the current buffer file is cleared.

[0182] After clearing the buffer file, wait for a new piece of recognition data in the buffer file, input the recognition data in the buffer file into the pre-established barrel position angle model, and obtain the current angle position information of the target feature;

[0183] Generate state information based on the current angular position information of the target feature, wherein the state information is used to indicate that the angular position information is the latest;

[0184] The angle position information and the state information are transmitted to the server side, and the server side moves the target feature of the barrel to the designated position according to the received angle position information and state information.

[0185] Furthermore, if the modules / units integrated in the electronic device 200 are implemented as software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0186] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0187] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for positioning drum features for a drum labeling machine, characterized in that: The steps include: Based on the preset multiple cameras, the video stream data of the barrel is obtained, and the pre-trained barrel feature recognition network is used to perform feature recognition on the video stream data. The recognized target features and the camera numbers corresponding to the target features are saved in a buffer file. Specifically: The target feature identified by the barrel feature recognition network is represented by a rectangular bounding box. The target feature information of the barrel is identified in the acquired video stream data. The bounding box is marked with the target category information and target confidence corresponding to the identified target feature; After identifying the bounding box corresponding to the target feature, according to the target category information and target confidence of the target, select the target whose target category information is the set category and the target confidence meets the set threshold, then use the target as the barrel target feature, and the bounding box corresponding to the target is the bounding box corresponding to the described barrel target feature; The camera number of the video stream data from which the target feature of the barrel is identified and the x-coordinate of the center coordinate of the bounding box are set as the identified feature information, wherein the identified feature information is the target feature and the camera number corresponding to the target feature; Initialize the buffer file. When the execution signal from the server is monitored, the command task is executed according to the execution signal and the current buffer file is cleared. After clearing the buffer file, wait for a new piece of recognition data in the buffer file, input the recognition data in the buffer file into the pre-established barrel position angle model to obtain the current angle position information of the target feature; the barrel position angle model is a model trained and fitted based on random forest regression, specifically: After obtaining the latest recognized barrel feature information in the buffer file, the camera number and the x-coordinate of the center coordinate of the bounding box in the feature information are input into the barrel position angle model to obtain the current angle position information of the barrel target feature; The barrel position angle model outputs position angle information ranging from 0° to 360°. The camera number of the identified barrel feature information and the x-coordinate of the center coordinate of the bounding box are input into the barrel feature position angle model. Regression analysis is performed based on multiple decision trees constructed by randomly selecting subsets and features from the dataset. Finally, a predicted position angle information is output. Generate state information based on the current angular position information of the barrel target feature, wherein the state information is used to indicate that the angular position information is the latest; The angle position information and the state information are transmitted to the server side, and the server side moves the target feature of the barrel to the designated position according to the received angle position information and state information.

2. The barrel feature positioning method for a barrel labeling machine according to claim 1, characterized in that: The video stream data of the barrel is obtained based on the preset multiple cameras, specifically: A camera is set up in four directions on the side of the barrel, and the video stream data of the side of the barrel is obtained by using the camera. Each camera covers a 90° range of the side of the barrel.

3. The barrel feature positioning method for a barrel labeling machine according to claim 1, characterized in that: The clearing of the buffer file and waiting for a piece of identification data in the buffer file is specifically: First, wait for the execution signal from the server and listen for the execution signal from the specified register on the server; After waiting for the execution signal given by the designated register on the server side, clear the buffer file storing the recognized barrel feature identification information, wherein the barrel feature identification information is the target feature and the camera number corresponding to the target feature; Finally, wait for the latest barrel feature recognition information to be saved in the buffer file.

4. The barrel feature positioning method for a barrel labeling machine according to claim 1, characterized in that: Input the recognition data in the buffer file into the pre-established barrel position angle model to obtain the current angle position information of the target feature, specifically: Initialize the position information, status information, waiting signal and real angle information of the buffer file; A plurality of identification data are input into the barrel position angle model to obtain a plurality of angle position information, and an average value of the plurality of angle position information is taken as the output of the barrel position angle model.

5. The barrel feature positioning method for a barrel labeling machine according to claim 1, characterized in that: After transmitting the position information and the status information to the server, it is first determined whether the transmitted angular position information is the latest. If it is the latest, the server moves the target feature of the barrel to the specified position; after the operation is completed, it waits for the next barrel to arrive at the specified position.

6. A barrel feature positioning system for a barrel labeling machine, applied to the barrel feature positioning method for a barrel labeling machine according to any one of claims 1 to 5, characterized in that: It includes a target feature acquisition module, an initialization module, an angle position information acquisition module, a state information acquisition module and a server execution module; The target feature acquisition module is used to acquire video stream data of the barrel based on a plurality of preset cameras, perform feature recognition on the video stream data using a pre-trained barrel feature recognition network, and save the recognized target features and the camera numbers corresponding to the target features into a buffer file; The initialization module is used to initialize the buffer file. When the execution signal of the server is monitored, the instruction task is executed according to the execution signal and the current buffer file is cleared. The angle position information acquisition module is used to clear the buffer file, wait for a new piece of identification data in the buffer file, input the identification data in the buffer file into the pre-established barrel position angle model, and obtain the current angle position information of the target feature; The state information acquisition module is used to generate state information based on the current angular position information of the barrel target feature, and the state information is used to indicate that the angular position information is the latest; The server execution module is used to transmit the angle position information and the status information to the server side, and the server side moves the target feature of the barrel to a specified position according to the received angle position information and status information.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to perform the drum feature positioning method for a drum labeling machine according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the barrel feature positioning method for a barrel labeling machine according to any one of claims 1 to 5 is implemented.

Images