Equipment protective cover missing detection method and device

Abstract

The embodiments of the present disclosure provide a method and apparatus for detecting the absence of a device protective cover. The method comprises: obtaining various protective cover image samples, training an initial protective cover recognition model, and generating a target protective cover recognition model; photographing the protective cover to be identified to obtain a current protective cover image; inputting the current protective cover image into the target protective cover recognition model, and using the target protective cover recognition model to detect and analyze the current protective cover image to determine whether the protective cover to be identified is missing. In this way, the device protective cover can be monitored in real time to promptly detect whether the device protective cover is abnormal, avoid the danger caused by the untimely detection of the missing device protective cover, and improve the detection efficiency of the missing device protective cover.

Description

Technical Field

[0001] The present disclosure relates to the technical field of equipment protective covers, and in particular to the technical field of equipment protective cover detection. Background Art

[0002] Currently, industrial equipment is often equipped with protective covers. These covers effectively prevent operators from touching moving parts during operation, preventing emergencies or dangers. If a protective cover falls off during production, it can not only damage the equipment but also pose a series of safety risks.

[0003] Existing technology primarily relies on manual inspections to detect shield detachment. Upon discovering a shield detachment, inspectors immediately shut down the equipment and notify relevant personnel for repairs. However, this method lacks real-time monitoring of the equipment's shields, making it difficult to detect abnormalities in a timely manner. Furthermore, it is dangerous for inspectors to approach equipment with a detached shield, which is time-consuming and labor-intensive. Summary of the Invention

[0004] The present disclosure provides a method, apparatus, device, and storage medium for detecting the absence of a device protective cover.

[0005] According to a first aspect of the present disclosure, a method for detecting the absence of a protective cover of a device is provided. The method comprises:

[0006] Obtain various protective shield image samples, train the initial protective shield recognition model, and generate the target protective shield recognition model;

[0007] Take a picture of the protective cover to be identified to obtain the current protective cover image;

[0008] The current protective cover image is input into the target protective cover recognition model, so as to use the target protective cover recognition model to detect and analyze the current protective cover image to determine whether the protective cover to be recognized is missing.

[0009] According to the above aspects and any possible implementation, an implementation is further provided, wherein the acquiring various protective cover image samples, training the initial protective cover recognition model, and generating the target protective cover recognition model includes:

[0010] Obtain various protective cover image samples at different time periods to form a sample set;

[0011] The sample set is input into an initial protective cover recognition model based on a deep learning convolutional neural algorithm, so as to train the initial protective cover recognition model by utilizing the annotations of the sample set and the features of the sample set, thereby obtaining the target protective cover recognition model.

[0012] According to the above aspects and any possible implementation, an implementation is further provided, wherein before inputting the sample set into the initial protective cover recognition model based on the deep learning convolutional neural algorithm, the method further includes:

[0013] Performing cropping processing on the protective cover image samples in the sample set according to preset requirements;

[0014] The protective cover contour in the protective cover image sample is annotated using an annotation tool.

[0015] According to the above aspects and any possible implementation, a further implementation is provided, wherein the detecting and analyzing the current protective cover image using the target protective cover recognition model to determine whether the protective cover to be recognized is missing includes:

[0016] Detecting the inner contour and the outer contour of the current protective cover image using the target protective cover recognition model;

[0017] If the inner contour and the outer contour are detected at the same time, it is determined that the protective cover to be identified is not missing;

[0018] If the inner contour and / or the outer contour is not detected, it is determined that the protective cover to be identified is missing.

[0019] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0020] When it is determined that the protective cover to be identified is missing, an alarm is issued, and the alarm includes at least one of the following: an audible and visual alarm, an image alarm, and an information push alarm.

[0021] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0022] When it is determined that the protective cover to be identified is missing, an instruction is sent to a controller of a device corresponding to the protective cover to be identified to control the device to shut down or adjust operating parameters.

[0023] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein photographing the protective cover to be identified to obtain a current protective cover image includes:

[0024] A camera is installed above the front of the protective cover to be identified to shoot the protection area of ​​the protective cover to be identified, wherein the camera supports RTSP and / or ONVIF protocol.

[0025] According to a second aspect of the present disclosure, a device for detecting the absence of a protective cover of an equipment is provided. The device comprises:

[0026] The first processing module is used to obtain various protective cover image samples, train the initial protective cover recognition model, and generate a target protective cover recognition model;

[0027] A shooting module is used to shoot the protective cover to be identified and obtain the current protective cover image;

[0028] The second processing module is used to input the current protective cover image into the target protective cover recognition model, so as to use the target protective cover recognition model to detect and analyze the current protective cover image and determine whether the protective cover to be recognized is missing.

[0029] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0030] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect and / or the second aspect of the present disclosure is implemented.

[0031] In the present disclosure, by training the initial protective cover recognition model, a target protective cover recognition model that can accurately identify whether the protective cover is missing can be generated. Then, the target protective cover recognition model is used to automatically detect and analyze the current protective cover image to automatically determine whether the protective cover to be identified is missing. This enables real-time monitoring of the equipment protective cover and timely detection of whether the equipment protective cover is abnormal, avoiding the danger caused by untimely detection of the missing equipment protective cover, and also improving the detection efficiency of the missing equipment protective cover.

[0032] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0034] Figure 1 A flow chart of a method for detecting missing protective cover of a device according to an embodiment of the present disclosure is shown;

[0035] Figure 2 A schematic diagram of marking the outline of a protective cover according to an embodiment of the present disclosure is shown.

[0036] Figure 3 A block diagram of a device for detecting missing protective cover of equipment according to an embodiment of the present disclosure is shown;

[0037] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0038] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0039] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0040] Figure 1 A flow chart of a device protective cover missing detection method 100 according to an embodiment of the present disclosure is shown.

[0041] The method 100 may include:

[0042] Step 110, obtaining various protective cover image samples, training an initial protective cover recognition model, and generating a target protective cover recognition model;

[0043] The protective cover image samples may be diverse, including images of protective covers of equipment of different types and sizes, or different images of the protective cover of the same equipment.

[0044] Step 120: photograph the protective cover to be identified to obtain a current protective cover image;

[0045] Step 130: Input the current protective cover image into the target protective cover recognition model, and use the target protective cover recognition model to detect and analyze the current protective cover image to determine whether the protective cover to be identified is missing. Missing protective covers to be identified include, but are not limited to, a device protective cover that has fallen off, or a device protective cover that has not fallen off but is missing a portion.

[0046] By training the initial protective cover recognition model, a target protective cover recognition model that can accurately identify whether the protective cover is missing can be generated. Then, the target protective cover recognition model is used to automatically detect and analyze the current protective cover image to automatically determine whether the protective cover to be identified is missing. This enables real-time monitoring of the equipment protective cover and timely detection of whether the equipment protective cover is abnormal, avoiding the danger caused by untimely detection of the missing equipment protective cover, and also improving the detection efficiency of the missing equipment protective cover.

[0047] In some embodiments, acquiring various protective cover image samples, training an initial protective cover recognition model, and generating a target protective cover recognition model includes:

[0048] Obtain various protective cover image samples at different time periods to form a sample set;

[0049] Different devices require different protective covers, so the protective cover image samples may be diverse. Even the protective cover of the same device may change over time. Therefore, protective cover image samples of different time periods can be obtained to form a sample set.

[0050] The pattern of the shield image sample may also be irregular.

[0051] The sample set is input into an initial protective shield recognition model based on a deep learning convolutional neural network algorithm, and the initial protective shield recognition model is trained using the annotations and features of the sample set to obtain the target protective shield recognition model. The features of the sample set include but are not limited to contour features, and may also include texture features, color features, shape features, etc.

[0052] By inputting a sample set formed by various protective cover image samples into the initial protective cover recognition model, the initial protective cover recognition model can be automatically trained using the annotations of the sample set and various features of the sample set, thereby obtaining a target protective cover recognition model, which facilitates the subsequent automatic detection of whether the protective cover is missing.

[0053] In some embodiments, before inputting the sample set into an initial protective shield recognition model based on a deep learning convolutional neural algorithm, the method further comprises:

[0054] Performing cropping processing on the protective cover image samples in the sample set according to preset requirements;

[0055] Use annotation tools to annotate the outline of the protective cover in the protective cover image sample, such as Figure 2 Marking the outline of the protective cover includes but is not limited to marking whether the outline of the protective cover is complete, and may also include marking key points (such as protrusions and depressions) on the equipment protective cover.

[0056] After obtaining the sample set, the protective cover image samples in the sample set can be cropped to make the size of the protective cover image samples more uniform, thereby improving the accuracy of model training. Then, the protective cover contours in the protective cover image samples can be automatically labeled using a labeling tool, so that the target protective cover recognition model can determine whether the protective cover is missing based on the contour features.

[0057] In some embodiments, the detecting and analyzing the current protective cover image using the target protective cover recognition model to determine whether the protective cover to be recognized is missing includes:

[0058] Detecting the inner contour and the outer contour of the current protective cover image using the target protective cover recognition model;

[0059] If the inner contour and the outer contour are detected at the same time, it is determined that the protective cover to be identified is not missing;

[0060] If the inner contour and / or the outer contour is not detected, it is determined that the protective cover to be identified is missing.

[0061] Since a normal protective cover has an inner contour and an outer contour, the target protective cover recognition model can be used to automatically detect the inner contour and outer contour of the current protective cover image. If the inner contour and the outer contour are detected at the same time, it means that the protective cover to be identified is complete and not missing. If the inner contour and / or the outer contour is not detected, it means that the protective cover to be identified is missing and incomplete.

[0062] In some embodiments, the method further comprises:

[0063] When it is determined that the protective cover to be identified is missing, an alarm is issued, and the alarm includes at least one of the following: an audible and visual alarm, an image alarm, and an information push alarm.

[0064] When it is determined that the protective cover to be identified is missing, an alarm can be automatically issued to prompt the staff to replace the protective cover as soon as possible and pay attention to work safety.

[0065] In some embodiments, the method further comprises:

[0066] When it is determined that the protective cover to be identified is missing, an instruction is sent to a controller of a device corresponding to the protective cover to be identified to control the device to shut down or adjust operating parameters.

[0067] When it is determined that the protective cover to be identified is missing, the equipment controller can send an instruction to promptly control the equipment to shut down or adjust the equipment's operating parameters to ensure the safety of the equipment covered by the protective cover and avoid the protective cover falling off and causing operational safety hazards.

[0068] In some embodiments, photographing the protective cover to be identified to obtain a current protective cover image includes:

[0069] A camera is installed above the front of the protective cover to be identified to shoot the protection area of ​​the protective cover to be identified, wherein the camera supports RTSP (Real Time Streaming Protocol) and / or ONVIF (Open Network Video Interface Forum) protocol.

[0070] By installing a camera above and in front of the protective cover to be identified, the protective area of ​​the protective cover to be identified can be fully photographed, thereby accurately obtaining the image of the protective cover.

[0071] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0072] The above is an introduction to the method embodiment. The following is a further explanation of the solution disclosed in the present disclosure through an apparatus embodiment.

[0073] Figure 3 FIG. 3 shows a block diagram of a device protective cover missing detection apparatus 300 according to an embodiment of the present disclosure. Figure 3 As shown, the apparatus 300 includes:

[0074] The first processing module 310 is used to obtain various protective cover image samples, train the initial protective cover recognition model, and generate a target protective cover recognition model;

[0075] The shooting module 320 is used to shoot the protective cover to be identified and obtain the current protective cover image;

[0076] The second processing module 330 is configured to input the current protective cover image into the target protective cover recognition model, so as to detect and analyze the current protective cover image using the target protective cover recognition model to determine whether the protective cover to be recognized is missing.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0078] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions.

[0079] Figure 4 A schematic block diagram of an electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0080] The device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. Various programs and data required for the operation of the device 400 can also be stored in the RAM 403. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0081] Various components in device 400 are connected to I / O interface 405, including an input unit 406, such as a keyboard, mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409, such as a network card, modem, wireless communication transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0082] The computing unit 401 may be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0083] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0084] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0086] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0087] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0088] A computing system may include clients and servers. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers and forming a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0089] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0090] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for detecting the absence of a protective cover of an equipment, characterized in that: include: Obtain various protective shield image samples, train the initial protective shield recognition model, and generate the target protective shield recognition model; Take a picture of the protective cover to be identified to obtain the current protective cover image; Inputting the current protective cover image into the target protective cover recognition model, so as to detect and analyze the current protective cover image using the target protective cover recognition model to determine whether the protective cover to be recognized is missing; The detecting and analyzing the current protective cover image using the target protective cover recognition model to determine whether the protective cover to be recognized is missing includes: Detecting the inner contour and the outer contour of the current protective cover image using the target protective cover recognition model; If the inner contour and the outer contour are detected at the same time, it is determined that the protective cover to be identified is not missing; If the inner contour and / or the outer contour is not detected, it is determined that the protective cover to be identified is missing; The method of obtaining various protective cover image samples, training the initial protective cover recognition model, and generating the target protective cover recognition model includes: Obtain various protective cover image samples at different time periods to form a sample set; Inputting the sample set into an initial protective cover recognition model based on a deep learning convolutional neural algorithm, so as to train the initial protective cover recognition model using the annotations of the sample set and the features of the sample set, thereby obtaining the target protective cover recognition model; Before inputting the sample set into an initial protective shield recognition model based on a deep learning convolutional neural algorithm, the method further includes: Performing cropping processing on the protective cover image samples in the sample set according to preset requirements; The protective cover contour in the protective cover image sample is marked by using a marking tool. The marking of the protective cover contour includes: marking whether the protective cover contour is complete, and marking key points on the protective cover.

2. The method according to claim 1, characterized in that The method further comprises: When it is determined that the protective cover to be identified is missing, an alarm is issued, and the alarm includes at least one of the following: an audible and visual alarm, an image alarm, and an information push alarm.

3. The method according to claim 1, characterized in that The method further comprises: When it is determined that the protective cover to be identified is missing, an instruction is sent to a controller of a device corresponding to the protective cover to be identified to control the device to shut down or adjust operating parameters.

4. The method according to any one of claims 1 to 3, characterized in that The step of photographing the protective cover to be identified to obtain a current protective cover image includes: A camera is installed above the front of the protective cover to be identified to shoot the protection area of ​​the protective cover to be identified, wherein the camera supports RTSP and / or ONVIF protocol.

5. A device for detecting missing equipment protective cover, characterized in that: include: The first processing module is used to obtain various protective cover image samples, train the initial protective cover recognition model, and generate a target protective cover recognition model; A shooting module is used to shoot the protective cover to be identified and obtain the current protective cover image; a second processing module, configured to input the current protective cover image into the target protective cover recognition model, so as to detect and analyze the current protective cover image using the target protective cover recognition model, and determine whether the protective cover to be recognized is missing; The second processing module is specifically configured to: Detecting the inner contour and the outer contour of the current protective cover image using the target protective cover recognition model; If the inner contour and the outer contour are detected at the same time, it is determined that the protective cover to be identified is not missing; If the inner contour and / or the outer contour is not detected, it is determined that the protective cover to be identified is missing; The first processing module is specifically used to: obtain various protective cover image samples in different time periods to form a sample set; Inputting the sample set into an initial protective cover recognition model based on a deep learning convolutional neural algorithm, so as to train the initial protective cover recognition model using the annotations of the sample set and the features of the sample set, thereby obtaining the target protective cover recognition model; Performing cropping processing on the protective cover image samples in the sample set according to preset requirements; The protective cover contour in the protective cover image sample is marked by using a marking tool. The marking of the protective cover contour includes: marking whether the protective cover contour is complete, and marking key points on the protective cover.

6. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

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