Factory operator-oriented instrument specification detection system and management method
The instrument specification detection system, which combines video capture and deep learning algorithms, solves the problems of low efficiency and false alarm rate in detecting the wearing of masks and safety helmets by factory workers, and achieves precise safety management and production quality assurance.
Patent Information
- Application Number
- CN202511455887.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-13
- Publication Date
- 2026-01-09
AI Technical Summary
In existing technologies, the detection of mask and safety helmet wearing by factory workers relies on manual inspection, which is inefficient, has a high rate of missed detection, lacks accurate judgment of dynamic behavior, and has a high rate of false alarms.
By employing video acquisition, face recognition, target detection, and action recognition modules, combined with OpenCV, YOLOv8, and the improved SlowFast algorithm, the system achieves accurate detection of whether workers are wearing masks and safety helmets and their dust removal actions, and introduces a dust-adhesive board positioning and graded alarm mechanism.
It enables real-time detection of workers' helmet and mask wearing status, reduces false alarm rate, improves detection and management efficiency, and ensures worker safety and production quality.
Smart Images
Figure CN121305643A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of this application relate to the field of industrial safety and intelligent management technology, and in particular to an instrument specification testing system and management method for factory operators. Background Technology
[0002] Industrial environments may contain metal dust, chemical dust, and mineral dust. Long-term inhalation can lead to occupational diseases such as pneumoconiosis and respiratory inflammation. Masks, through their filter layers, block dust from entering the respiratory tract, serving as the first line of defense for workers' respiratory health. In industrial workplaces, incidents such as falling objects (e.g., tools, parts), mechanical collisions, and head contact with hard objects are frequent. Safety helmets, through their impact-resistant outer shell and cushioning inner lining, effectively reduce the risk of head injuries and can even save lives in critical moments.
[0003] In industrial environments, workers wearing masks and safety helmets are basic safety requirements, and the pre-entry dust removal process can effectively reduce dust pollution. Traditional management relies on manual inspections or card records, which suffers from low efficiency and high missed detection rates. Among existing technologies, protective equipment identification based on target detection has been applied, but it lacks accurate judgment of dynamic behaviors (such as dust removal actions) and does not incorporate area constraints (such as the location of the dust-adhesive board), resulting in a high false alarm rate. Summary of the Invention
[0004] In view of this, the present application provides an instrument specification testing solution for factory workers, which can accurately detect whether personnel are wearing masks and safety helmets and whether they have completed the dust removal action, thus ensuring both personnel safety and production quality.
[0005] In a first aspect of this application, an instrument specification testing system for factory operators is provided, comprising: Video capture module, face recognition module, target detection module, and action recognition module; The video acquisition module is deployed at the factory entrance and key areas to acquire real-time video data; The face recognition module is used to process video data based on the OpenCV algorithm to identify workers' facial information; The target detection module is used to process video data based on the YOLOv8 algorithm to detect whether workers are wearing masks and safety helmets; The action recognition module is used to analyze video sequences based on the improved SlowFast algorithm to determine whether the worker has completed the dust removal action on the ash-sticking board.
[0006] In some embodiments, the face recognition module includes: The face input submodule is used to input workers' facial information and employee ID permission information; The face recognition submodule is used to correctly identify workers' faces; The Face Deletion and Modification submodule is used to delete or modify incorrectly entered face information and permission information.
[0007] In some embodiments, the target detection module includes: The mask detection submodule is used to identify the worker's facial area and determine the mask wearing status; The safety helmet detection submodule is used to detect whether workers are wearing safety helmets on their head area; The plastering board positioning submodule is used to identify the position of the plastering board in the video, providing regional constraints for motion recognition; The duration determination submodule is used to verify whether the stepping action lasts for a preset duration.
[0008] In some embodiments, the action recognition module includes: The time-series analysis submodule is used to extract the motion features of workers in continuous video frames to determine whether there is a dust-cleaning action. The location matching submodule is used to combine the coordinates of the ash-sticking board to ensure that the ash-cleaning action occurs within the specified area.
[0009] In some embodiments, it also includes: An alarm module is provided to trigger an alarm when it is detected that a mask and / or safety helmet are not being worn, or when a dust removal operation is not completed.
[0010] In some embodiments, the alarm module supports tiered alarms: When someone is detected not wearing a mask or helmet, a Level 1 alarm is triggered, with an audible alarm and the person's identity flagged. When incomplete dust removal is detected, a level 2 alarm is triggered, an indicator light is displayed, and access to the factory is suspended.
[0011] In some embodiments, it also includes: The QT5 integrated module encapsulates various functional modules and provides a visual interface, supporting real-time monitoring and system management.
[0012] In a second aspect of this application, a method for managing instrument specification testing for factory operators is provided, comprising: Acquire real-time video data of the factory entrance and key areas; The video data at the factory entrance was processed using the OpenCV algorithm to identify workers' facial information; The YOLOv8 algorithm is used to process video data in key areas to detect whether workers are wearing masks and safety helmets. Based on the improved SlowFast algorithm, the video sequence of video data in key areas is analyzed to determine whether the worker has completed the dust removal action on the ash-sticking board. An alarm is triggered in response to the detection of invalid worker identification information, and / or the worker not wearing a mask or safety helmet, and / or the worker not completing the dust removal action on the ash board.
[0013] In a third aspect of this application, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.
[0014] In a fourth aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method as described in the second aspect of this application.
[0015] The instrument specification inspection system for factory workers provided in this application includes: a video acquisition module, a face recognition module, a target detection module, and a motion recognition module. The video acquisition module is deployed at the factory entrance and key areas to acquire real-time video data. The face recognition module processes the video data based on the OpenCV algorithm to identify worker facial information. The target detection module processes the video data based on the YOLOv8 algorithm to detect whether workers are wearing masks and safety helmets. The motion recognition module analyzes the video sequence based on the improved SlowFast algorithm to determine whether workers have completed the dust removal action on the dust removal board. By detecting personnel safety measures and standardized behaviors, production quality can be guaranteed while ensuring personnel safety.
[0016] It should be understood that the description in the Summary Section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to restrict the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description
[0017] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A system architecture diagram relating to the methods provided in the embodiments of this application; Figure 2 A block diagram of an instrument specification testing system for factory workers according to an embodiment of this application; Figure 3 A flowchart illustrating an instrument specification testing and management method for factory workers according to an embodiment of this application; Figure 4 A schematic diagram of the structure of a terminal device or server suitable for implementing the embodiments of this application; Figure 5 This is a schematic diagram of the testing process for the instrument specification testing and management method for factory operators, as described in an embodiment of this application. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0019] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0020] Figure 1 A schematic diagram of an exemplary operating environment 100 in which embodiments of the present disclosure can be implemented is shown. The operating environment 100 includes a client 110, a communication module 120, a server 130, and a device 140.
[0021] Among them, client 110 can be the current user terminal, used to report instructions to server 130; Server 130 can be a cloud server used to maintain information about currently connected devices in the cloud, such as the online / offline status and device lock status of devices. When a device goes online or offline, the online / offline status of the device is updated, and the device lock status information is maintained. Furthermore, server 130 also includes: The device locking module is used to issue lock / unlock tasks to device 140, ensuring the consistency of device 140's status with that of server 130. When device 140 is offline, its lock status is synchronized to server 130, and a lock task is created. When device 140 comes online, the lock task is issued. The lock task is completed when device 140 returns a successful lock status.
[0022] Device 140 can be a target user terminal, used to receive instructions issued by server 130, perform corresponding operations according to the instructions, and report operation result information to server 130.
[0023] The communication module 120 is used to maintain the communication link between the device 140 and the server 130. When the device 140 goes online or offline, it reports callback information to the server 130. That is, the server 130 can connect to the device 140 through the communication module 120.
[0024] Figure 2 A block diagram of an instrument specification testing system for factory workers according to an embodiment of this application is shown, the system comprising: Video capture module 201, face recognition module 202, target detection module 203, and action recognition module 204; The video acquisition module 201 is deployed at the factory entrance and key areas to acquire real-time video data; The face recognition module 202 is used to process video data based on the OpenCV algorithm to identify the worker's face information; The target detection module 203 is used to process video data based on the YOLOv8 algorithm to detect whether workers are wearing masks and safety helmets; The action recognition module 204 is used to analyze video sequences based on the improved SlowFast algorithm to determine whether the worker has completed the dust removal action on the ash-sticking board.
[0025] The OpenCV, YOLOv8, and SlowFast algorithms in this embodiment are all deep learning algorithms. The application is selected according to the specific scenario in this embodiment. The specific implementation process of the algorithm will not be elaborated in this embodiment.
[0026] The instrument specification inspection system for factory workers in this embodiment can ensure production quality while guaranteeing personnel safety by detecting personnel safety measures and standardized behaviors.
[0027] As an optional embodiment of this application, in the above embodiment, the face recognition module includes: The face input submodule is used to input workers' facial information and employee ID permission information; The face recognition submodule is used to correctly identify workers' faces; The Face Deletion and Modification submodule is used to delete or modify incorrectly entered face information and permission information.
[0028] Specifically, factory personnel can pre-register their facial images using a facial recognition module. Management can then assign corresponding employee numbers and permissions based on these images. When personnel enter the factory, the facial recognition submodule identifies their faces, triggering an alarm if an unregistered facial image is detected. If a personnel leaves the company or their facial image cannot be correctly recognized, the facial deletion / modification submodule can delete or modify incorrectly entered facial information and permission settings.
[0029] The target detection module includes: The mask detection submodule is used to identify the worker's facial area and determine the mask wearing status; The safety helmet detection submodule is used to detect whether workers are wearing safety helmets on their head area; The plastering board positioning submodule is used to identify the position of the plastering board in the video, providing regional constraints for motion recognition; The duration determination submodule is used to verify whether the stepping action lasts for a preset duration.
[0030] In this embodiment, different sub-modules are used to detect corresponding images, thereby performing comprehensive detection of personnel images.
[0031] The action recognition module includes: The time-series analysis submodule is used to extract the motion features of workers in continuous video frames to determine whether there is a dust-cleaning action. The location matching submodule is used to combine the coordinates of the ash-sticking board to ensure that the ash-cleaning action occurs within the specified area.
[0032] Through the coordinated detection of facial recognition, target detection, and action recognition modules, the system can automatically detect the identity information of factory personnel, whether they are wearing masks and / or safety helmets, and whether they have completed the dust removal process.
[0033] Furthermore, as an optional embodiment of this application, the above embodiments also include: An alarm module is provided to trigger an alarm when it is detected that a mask and / or safety helmet are not being worn, or when a dust removal operation is not completed.
[0034] The alarm module supports tiered alarms: When someone is detected not wearing a mask or helmet, a Level 1 alarm is triggered, with an audible alarm and the person's identity flagged. When incomplete dust removal is detected, a level 2 alarm is triggered, an indicator light is displayed, and access to the factory is suspended.
[0035] This application's embodiment of the instrument compliance detection system for factory workers uses the YOLOv8 algorithm to detect the wearing status of masks and safety helmets in real time, and combines it with an improved SlowFast algorithm to verify the compliance of disinfection and dust removal actions. The system introduces a dust-adhesive board positioning module to ensure that actions are only completed within authorized areas, and accurately determines the duration of actions through time sequence analysis, significantly reducing the false alarm rate.
[0036] In addition, in some embodiments, it also includes: The QT5 integrated module encapsulates various functional modules and provides a visual interface, supporting real-time monitoring and system management.
[0037] The QT5 interface can display detection results and alarm information in real time, and supports administrators to adjust detection thresholds or query historical records.
[0038] This application's embodiments accurately identify disinfection and dust removal actions through regional constraints and time-series analysis; a tiered alarm mechanism improves management efficiency; and a visual interface reduces operational complexity and adapts to different factory scenarios.
[0039] It should be noted that, for the sake of simplicity, the aforementioned system embodiments are all described as functional combinations of a series of modules. However, those skilled in the art should understand that this application is not limited to the described module functions, because according to this application, some modules can be integrated into the same module, and the function of the same module can be implemented by multiple modules. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the modules involved are not necessarily essential to this application.
[0040] The above is an introduction to the method embodiments. The following method embodiments will further illustrate the solution described in this application.
[0041] Figure 3 This is a flowchart illustrating an instrument specification testing and management method for factory operators according to an embodiment of this application. The instrument specification testing and management method for factory operators according to this embodiment may include the following steps: S301: Acquire real-time video data of the factory entrance and key areas; S302: Process video data at the factory entrance based on the OpenCV algorithm to identify worker facial information; S303: Based on the YOLOv8 algorithm, video data in key areas is processed to detect whether workers are wearing masks and safety helmets; S304: Based on the improved SlowFast algorithm, the video sequence of video data in the key area is analyzed to determine whether the worker has completed the dust removal action on the ash-sticking board. S305: An alarm is triggered in response to the detection of invalid worker identification information, and / or the worker not wearing a mask or safety helmet, and / or the worker not completing the dust removal action on the ash removal board.
[0042] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific implementation process of the described method can be referred to the corresponding process in the foregoing system embodiments, and will not be repeated here.
[0043] The technical solution of this application will be illustrated below with a specific application example. For example... Figure 5 The diagram illustrates the detection process of an instrument specification testing and management method for factory workers according to an embodiment of this application. In this embodiment, the video acquisition module collects video data of factory personnel entering the factory and video data of designated areas. The face recognition module identifies the face images in the collected video data to determine the identity of the workers and whether their identity information is legitimate. The target detection module detects the presence of masks, safety helmets, and dust removal boards in the video data, thereby detecting whether factory personnel are wearing masks and hats (safety helmets). If no masks and hats are detected, an alarm command is sent to the alarm module. Within the dust removal board area, the actions of factory personnel are identified to determine whether a dust-collecting action lasting 5 seconds or more is performed. If no dust-collecting action lasting 5 seconds or more is performed, an alarm command is sent to the alarm module. If a dust-collecting action lasting 5 seconds or more is performed, it is recorded by the recording module. Simultaneously, the alarm history data of the alarm module is also sent to the recording module for recording.
[0044] The method in this application embodiment can achieve similar technical effects to the above embodiments, and will not be repeated here.
[0045] Figure 4 A schematic diagram of a terminal device or server suitable for implementing embodiments of this application is shown.
[0046] like Figure 4 As shown, the terminal device or server includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from storage section 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the terminal device or server. The CPU 401, ROM 402, and RAM 403 are interconnected via bus 404. An input / output (I / O) interface 405 is also connected to bus 404.
[0047] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.
[0048] Specifically, according to embodiments of this application, the above method flow steps can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined in the system of this application.
[0049] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0050] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0051] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.
[0052] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the methods described in this application.
[0053] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.
Claims
1. An instrument specification testing system for factory operators, characterized in that, include: Video capture module, face recognition module, target detection module, and action recognition module; The video acquisition module is deployed at the factory entrance and key areas to acquire real-time video data; The face recognition module is used to process video data based on the OpenCV algorithm to identify workers' facial information; The target detection module is used to process video data based on the YOLOv8 algorithm to detect whether workers are wearing masks and safety helmets; The action recognition module is used to analyze video sequences based on the improved SlowFast algorithm to determine whether the worker has completed the dust removal action on the ash-sticking board.
2. The system according to claim 1, characterized in that, The face recognition module includes: The face input submodule is used to input workers' facial information and employee ID permission information; The face recognition submodule is used to correctly identify workers' faces; The Face Deletion and Modification submodule is used to delete or modify incorrectly entered face information and permission information.
3. The system according to claim 1, characterized in that, The target detection module includes: The mask detection submodule is used to identify the worker's facial area and determine the mask wearing status; The safety helmet detection submodule is used to detect whether workers are wearing safety helmets on their head area; The plastering board positioning submodule is used to identify the position of the plastering board in the video, providing regional constraints for motion recognition; The duration determination submodule is used to verify whether the stepping action lasts for a preset duration.
4. The system according to claim 1, characterized in that, The action recognition module includes: The time-series analysis submodule is used to extract the motion features of workers in continuous video frames to determine whether there is a dust-cleaning action. The location matching submodule is used to combine the coordinates of the ash-sticking board to ensure that the ash-cleaning action occurs within the specified area.
5. The system according to claim 1, characterized in that, Also includes: An alarm module is provided to trigger an alarm when it is detected that a mask and / or safety helmet are not being worn, or when a dust removal operation is not completed.
6. The system according to claim 5, characterized in that, The alarm module supports tiered alarms: When someone is detected not wearing a mask or helmet, a Level 1 alarm is triggered, with an audible alarm and the person's identity flagged. When incomplete dust removal is detected, a level 2 alarm is triggered, an indicator light is displayed, and access to the factory is suspended.
7. The system according to claim 1, characterized in that, Also includes: The QT5 integrated module encapsulates various functional modules and provides a visual interface, supporting real-time monitoring and system management.
8. A method for managing instrument specification testing for factory operators, characterized in that, include: Acquire real-time video data of the factory entrance and key areas; The video data at the factory entrance was processed using the OpenCV algorithm to identify workers' facial information; The YOLOv8 algorithm is used to process video data in key areas to detect whether workers are wearing masks and safety helmets. Based on the improved SlowFast algorithm, the video sequence of video data in key areas is analyzed to determine whether the worker has completed the dust removal action on the ash-sticking board. An alarm is triggered in response to the detection of invalid worker identification information, and / or the worker not wearing a mask or safety helmet, and / or the worker not completing the dust removal action on the ash board.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method as described in claim 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in claim 8.