CN116778276A - Safe production model training method, application method, device, equipment and medium - Google Patents
Safe production model training method, application method, device, equipment and medium Download PDFInfo
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Abstract
The application discloses a safe production model training method, an application method, a device, equipment and a medium. The safety production model comprises a plurality of edge detection models and a center detection model, in the actual detection process, the detection results of the edge detection models and the detection results of the center detection models are compared, when the two results are inconsistent, the on-site actual safety results are obtained, the model update training is carried out on the edge detection models or the center detection models based on the on-site actual safety results, the edge detection models and the center detection models are continuously collected by using an incremental learning method to judge erroneous data, and the erroneous data are used for iterative update of the models, so that the high precision of the edge detection models and the high universality of the center detection models are realized, the problem that the single point data volume is insufficient to train a model with better performance is effectively solved, the accuracy and the robustness of the model are improved, and the monitoring is more accurate and reliable.
Description
Technical Field
The present application relates to computer technology, and in particular, to a method, an application method, a device, equipment, and a medium for training a safe production model.
Background
With the continuous development of artificial intelligence, computer vision technology is also increasingly widely applied in the field of safety production. The monitoring of hazards such as fire and smoke, the monitoring of dangerous behaviors of personnel, the monitoring of dangerous chemicals and the like are all independent of the support of computer vision technology.
In order to realize real-time monitoring, the image is often acquired by a camera, and the scene is inferred frame by frame. However, this requires that the model must be lightweight, and also allows for the use of different scenes, so that a specific model for different camera points is required. However, the amount of data collected by a single point location is insufficient to train a better performing model.
Disclosure of Invention
The application provides a safe production model training method, an application method, a device, equipment and a medium, which are used for solving the problem that single point data volume is insufficient to train a model with better performance, improving the accuracy and the robustness of the model and enabling the monitoring to be more accurate and reliable.
In a first aspect, the present application provides a method for training a safety production model, the safety production model including a plurality of edge detection models and a center detection model, the method comprising:
acquiring an image of a production site acquired by image acquisition equipment;
inputting the image into a corresponding edge detection model for processing to obtain an edge model detection result;
when the edge model detection result is that potential safety hazards exist, inputting the image into a center detection model for processing to obtain a center model detection result;
when the center model detection result shows that potential safety hazards exist, a safety alarm prompt is sent out;
when the center model detection result is that no potential safety hazard exists, acquiring an on-site actual safety result;
when the actual safety result of the site is that potential safety hazards exist, the image is used as a training sample to update and train the center detection model;
and when the actual safety result on the site is that no potential safety hazard exists, updating and training the edge detection model by taking the image as a training sample.
Optionally, the safe production model training method further comprises:
when the edge model detection result is that no potential safety hazard exists, acquiring an edge model detection result in a preset time period before the current time node;
and when the edge model detection results within the preset time period are all potential safety hazards, reducing the judgment threshold value of the edge detection model.
Optionally, the detection result of the potential safety hazard includes at least one of the following:
the method comprises the steps of enabling an object to be identified to have illegal appearance information, enabling the object to be identified to have illegal behaviors, enabling the type of an illegal vehicle to be changed, enabling the vehicle to drive into a forbidden area, enabling production equipment to have abnormal states, enabling smoke and fire to be present and enabling the object to be identified to have legacy.
Optionally, updating and training the center detection model by using the image as a training sample includes:
when the actual safety result of the site is that potential safety hazards exist, storing the image into a first database;
judging whether the number of the images in the first database reaches a first threshold value or not;
if yes, training the center detection model by taking the images in the first database as a training set, and updating model parameters of the center detection model.
Optionally, updating and training the edge detection model by using the image as a training sample includes:
when the actual safety result of the site is that no potential safety hazard exists, storing the image into a second database;
judging whether the number of images in the second database reaches a second threshold value or not;
if yes, the image in the second database is used as a training set, the edge detection model is trained, and model parameters of the edge detection model are updated.
Optionally, the model architecture of the edge detection model is smaller than the center detection model.
In a second aspect, the present application further provides a safety production detection method, which is applied to the safety production model trained by the safety production model training method provided in the first aspect of the present application.
In a third aspect, the present application also provides a safety production model training apparatus, the safety production model including a plurality of edge detection models and a center detection model, the apparatus comprising:
the image acquisition module is used for acquiring the image of the production site acquired by the image acquisition equipment;
the first detection module is used for inputting the image into a corresponding edge detection model for processing to obtain an edge model detection result;
the second detection module is used for inputting the image into a center detection model for processing when the edge model detection result is that the potential safety hazard exists, so as to obtain a center model detection result;
the alarm prompt module is used for sending out a safety alarm prompt when the detection result of the central model is that the potential safety hazard exists;
the actual result acquisition module is used for acquiring an on-site actual safety result when the center model detection result is that the potential safety hazard does not exist;
the first updating training module is used for updating and training the center detection model by taking the image as a training sample when the on-site actual safety result is that potential safety hazards exist;
and the second updating training module is used for updating and training the edge detection model by taking the image as a training sample when the on-site actual safety result is that no potential safety hazard exists.
In a fourth aspect, the present application also provides an electronic device, including:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the safe production model training method as provided in the first aspect of the present application.
In a fifth aspect, the present application also provides a computer readable storage medium having stored therein computer executable instructions which when executed by a processor are for implementing a safe production model training method as provided in the first aspect of the present application.
The application provides a safe production model training method, which comprises a plurality of edge detection models and a center detection model, wherein the method comprises the following steps: the method comprises the steps of obtaining an image of a production site, which is acquired by image acquisition equipment, inputting the image into a corresponding edge detection model to be processed, obtaining an edge model detection result, inputting the image into a center detection model to be processed when the edge model detection result is a potential safety hazard, obtaining a center model detection result, sending a safety alarm prompt when the center model detection result is the potential safety hazard, obtaining a site actual safety result when the center model detection result is the potential safety hazard, updating and training the center detection model by taking the image as a training sample when the site actual safety result is the potential safety hazard, and updating and training the edge detection model by taking the image as the training sample when the site actual safety result is the potential safety hazard. In the application, in the actual detection process, the detection result of the edge detection model and the detection result of the center detection model are compared, when the two results are inconsistent, the on-site actual safety result is obtained, the model update training is carried out on the edge detection model or the center detection model based on the on-site actual safety result, the data of the error judgment of the edge detection model and the center detection model are continuously collected by using an incremental learning method, and are used for the iterative update of the model, thereby realizing the high precision of the edge detection model and the high universality of the center detection model, effectively solving the problem that the single point data amount is insufficient to train a model with better performance, improving the accuracy and the robustness of the model, and ensuring the monitoring to be more accurate and reliable.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for training a safe production model according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a training device for a safe production model according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of a safe production model training method provided by an embodiment of the present application, where the embodiment is applicable to a case of performing incremental training on a safe production model, and the method may be performed by a safe production model training device provided by the embodiment of the present application, where the device may be implemented by software and/or hardware, and is generally configured in an electronic device, as shown in fig. 1, and the safe production model training method specifically includes the following steps:
s101, acquiring an image of a production site acquired by an image acquisition device.
In the embodiment of the application, the image acquisition equipment is arranged on a production site and used for acquiring the image of the production site and uploading the image to the electronic equipment. The image capturing device may be a camera, and the captured image may be a picture or a video, and embodiments of the present application are not limited herein.
S102, inputting the image into an edge detection model for processing to obtain an edge model detection result.
In the embodiment of the application, the safety production model comprises a plurality of edge detection models and a center detection model, wherein the edge detection models are deployed in corresponding edge devices, and the center detection model is deployed in a center server. Each edge detection model corresponds to the use requirements of different scenes.
After the image of the production site acquired by the image acquisition equipment is acquired, inputting the image into a corresponding edge detection model for processing, and obtaining an edge model detection result. The image processing process by the edge detection model may include preprocessing, image segmentation, feature extraction, feature fusion, feature space mapping, and the like, which is not limited in this embodiment of the present application. The edge model detection result indicates whether potential safety hazards exist in the current production field, and the detection result of the potential safety hazards comprises at least one of the following: the object to be identified has illegal appearance information, the object to be identified has illegal behaviors, the type of the illegal vehicles, the vehicles drive into the forbidden area, the production equipment has abnormal states, the smoke and fire exist, the legacy exists and the like.
And S103, inputting the image into a center detection model for processing when the detection result of the edge model is that the potential safety hazard exists, and obtaining a center model detection result.
When the edge model detection result is that potential safety hazards exist, the image is input into a center detection model for processing, and further judgment is carried out, so that the center model detection result is obtained.
The image processing process by the center detection model may include preprocessing, image segmentation, feature extraction, feature fusion, feature space mapping, and the like, which is not limited in this embodiment of the present application. The center model detection result indicates whether potential safety hazards exist in the current production site, and the detection result of the potential safety hazards comprises at least one of the following: the object to be identified has illegal appearance information, the object to be identified has illegal behaviors, the type of the illegal vehicles, the vehicles drive into the forbidden area, the production equipment has abnormal states, the smoke and fire exist, the legacy exists and the like.
When the edge model detection result is that no potential safety hazard exists, the edge model detection result in the preset time period (for example, 2 days) before the current time node is obtained, and when the edge model detection results in the preset time period are all that no potential safety hazard exists, the judgment threshold value of the edge detection model is set to be higher, so that the edge detection model cannot normally detect the structure with the potential safety hazard, and at the moment, the judgment threshold value of the edge detection model is reduced, and the sensitivity of the edge detection model is improved.
In some embodiments of the present application, since the computing processing capability of the edge device is generally weak, in the embodiments of the present application, the model architecture of the edge detection model is smaller than that of the center detection model, so as to reduce the data processing pressure of the edge detection model and improve the detection efficiency of the edge device.
S104, when the center model detection result is that the potential safety hazard exists, a safety alarm prompt is sent out.
In the embodiment of the application, the edge model detection result is compared with the center model detection result, if the edge model detection result is that the potential safety hazard exists and the center model detection result is that the potential safety hazard exists, the potential safety hazard is considered to exist, and the alarm equipment is controlled to send out a safety alarm prompt.
S105, acquiring an actual safety result of the site when the center model detection result is that the potential safety hazard does not exist.
And when the central model detection result is that no potential safety hazard exists, acquiring an actual safety result on site. For example, a field actual security result may be determined by a field person in person at the field and the result entered into an electronic device.
S106, when the actual safety result on site is that potential safety hazards exist, the image is used as a training sample to update and train the center detection model.
When the on-site actual safety result is that potential safety hazards exist, the fact that the center detection model has missed detection is indicated, and the image is used as a training sample to update and train the center detection model so as to improve the detection accuracy of the center detection model.
In some embodiments of the present application, when the actual safety result in the field is that there is a safety hazard, the images are stored in the first database, then whether the number of the images in the first database reaches the first threshold is determined, if yes, the images in the first database are used as a training set to train the center detection model, and model parameters of the center detection model are updated. Specifically, a mode of calculating a loss value can be adopted to determine whether the center detection model converges, and when the loss value is smaller than a preset value, the center detection model is determined to converge.
And S107, when the actual safety result on site is that no potential safety hazard exists, the image is used as a training sample to update and train the edge detection model.
When the on-site actual safety result is that no potential safety hazard exists, the false judgment exists in the edge detection model, and the image is used as a training sample to update and train the edge detection model so as to improve the detection accuracy of the edge detection model.
In some embodiments of the present application, when the actual safety result in the field is that there is no safety hidden trouble, the image is stored in the second database, then it is determined whether the number of images in the second database reaches the second threshold, if yes, the image in the second database is used as a training set, the edge detection model is trained, and the model parameters of the edge detection model are updated. Specifically, a mode of calculating a loss value can be adopted to determine whether the center detection model converges, and when the loss value is smaller than a preset value, the center detection model is determined to converge.
The safety production model training method provided by the embodiment of the application comprises a plurality of edge detection models and a center detection model, and the method comprises the following steps: the method comprises the steps of obtaining an image of a production site, which is acquired by image acquisition equipment, inputting the image into a corresponding edge detection model to be processed, obtaining an edge model detection result, inputting the image into a center detection model to be processed when the edge model detection result is a potential safety hazard, obtaining a center model detection result, sending a safety alarm prompt when the center model detection result is the potential safety hazard, obtaining a site actual safety result when the center model detection result is the potential safety hazard, updating and training the center detection model by taking the image as a training sample when the site actual safety result is the potential safety hazard, and updating and training the edge detection model by taking the image as the training sample when the site actual safety result is the potential safety hazard. In the application, in the actual detection process, the detection result of the edge detection model and the detection result of the center detection model are compared, when the two results are inconsistent, the on-site actual safety result is obtained, the model update training is carried out on the edge detection model or the center detection model based on the on-site actual safety result, the data of the error judgment of the edge detection model and the center detection model are continuously collected by using an incremental learning method, and are used for the iterative update of the model, thereby realizing the high precision of the edge detection model and the high universality of the center detection model, effectively solving the problem that the single point data amount is insufficient to train a model with better performance, improving the accuracy and the robustness of the model, and ensuring the monitoring to be more accurate and reliable.
The embodiment of the application also provides a safety production detection method which is applied to the safety production model trained by the safety production model training method provided by any embodiment of the application.
Fig. 2 is a schematic structural diagram of a safety production model training device according to an embodiment of the present application, where the safety production model includes a plurality of edge detection models and a center detection model, and as shown in fig. 2, the safety production model training device includes:
an image acquisition module 201, configured to acquire an image of a production site acquired by an image acquisition device;
the first detection module 202 is configured to input the image into a corresponding edge detection model for processing, so as to obtain an edge model detection result;
the second detection module 203 is configured to input the image into a center detection model for processing when the edge model detection result is that there is a potential safety hazard, so as to obtain a center model detection result;
the alarm prompting module 204 is configured to issue a safety alarm prompt when the center model detection result indicates that a safety hidden danger exists;
the actual result obtaining module 205 is configured to obtain an on-site actual safety result when the center model detection result is that no potential safety hazard exists;
a first update training module 206, configured to update and train the center detection model by using the image as a training sample when the on-site actual safety result is that there is a safety hidden danger;
and a second update training module 207, configured to update and train the edge detection model by using the image as a training sample when the on-site actual safety result is that no safety hidden danger exists.
In some embodiments of the application, the safety production model training apparatus further comprises:
the detection result acquisition module is used for acquiring an edge model detection result within a preset time before the current time node when the edge model detection result is that no potential safety hazard exists;
and the threshold adjustment module is used for reducing the judgment threshold of the edge detection model when the edge model detection results within the preset time period are all potential safety hazards.
In some embodiments of the present application, the detection result of the existence of the potential safety hazard includes at least one of the following:
the method comprises the steps of enabling an object to be identified to have illegal appearance information, enabling the object to be identified to have illegal behaviors, enabling the type of an illegal vehicle to be changed, enabling the vehicle to drive into a forbidden area, enabling production equipment to have abnormal states, enabling smoke and fire to be present and enabling the object to be identified to have legacy.
In some embodiments of the present application, the first update training module 206 includes:
the first storage unit is used for storing the image into a first database when the actual safety result of the site is that potential safety hazards exist;
a first judging unit, configured to judge whether the number of images in the first database reaches a first threshold;
and the first updating training unit is used for taking the images in the first database as a training set when the number of the images in the first database reaches a first threshold value, training the center detection model and updating the model parameters of the center detection model.
In some embodiments of the application, the second update training module 207 comprises:
the second storage unit is used for storing the image into a second database when the actual safety result of the site is that no potential safety hazard exists;
a second judging unit, configured to judge whether the number of images in the second database reaches a second threshold;
and the second updating training unit is used for taking the images in the second database as a training set when the number of the images in the second database reaches a second threshold value, training the edge detection model and updating the model parameters of the edge detection model.
Optionally, the model architecture of the edge detection model is smaller than the center detection model.
The safety production model training device can execute the safety production model training method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of executing the safety production model training method.
Fig. 3 is a schematic diagram of an electronic device provided by an embodiment of the present application, which is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 3, the electronic device includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the safe production model training method.
In some embodiments, the secure production model training method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more of the steps of the safe production model training method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the secure production model training method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, 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: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
Embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements a safe production model training method as provided by any of the embodiments of the present application.
Computer program product in the implementation, the computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present application are achieved, and the present application is not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (10)
1. A method of training a safe production model, the safe production model comprising a plurality of edge detection models and a center detection model, the method comprising:
acquiring an image of a production site acquired by image acquisition equipment;
inputting the image into a corresponding edge detection model for processing to obtain an edge model detection result;
when the edge model detection result is that potential safety hazards exist, inputting the image into a center detection model for processing to obtain a center model detection result;
when the center model detection result shows that potential safety hazards exist, a safety alarm prompt is sent out;
when the center model detection result is that no potential safety hazard exists, acquiring an on-site actual safety result;
when the actual safety result of the site is that potential safety hazards exist, the image is used as a training sample to update and train the center detection model;
and when the actual safety result on the site is that no potential safety hazard exists, updating and training the edge detection model by taking the image as a training sample.
2. The safe production model training method of claim 1, further comprising:
when the edge model detection result is that no potential safety hazard exists, acquiring an edge model detection result in a preset time period before the current time node;
and when the edge model detection results within the preset time period are all potential safety hazards, reducing the judgment threshold value of the edge detection model.
3. The method of claim 1, wherein the detection of the presence of a potential safety hazard comprises at least one of:
the method comprises the steps of enabling an object to be identified to have illegal appearance information, enabling the object to be identified to have illegal behaviors, enabling the type of an illegal vehicle to be changed, enabling the vehicle to drive into a forbidden area, enabling production equipment to have abnormal states, enabling smoke and fire to be present and enabling the object to be identified to have legacy.
4. A method of training a safe production model according to any one of claims 1 to 3, wherein updating the central detection model using the image as a training sample comprises:
when the actual safety result of the site is that potential safety hazards exist, storing the image into a first database;
judging whether the number of the images in the first database reaches a first threshold value or not;
if yes, training the center detection model by taking the images in the first database as a training set, and updating model parameters of the center detection model.
5. A method of training a safe production model according to any one of claims 1 to 3, wherein updating the edge detection model using the image as a training sample comprises:
when the actual safety result of the site is that no potential safety hazard exists, storing the image into a second database;
judging whether the number of images in the second database reaches a second threshold value or not;
if yes, the image in the second database is used as a training set, the edge detection model is trained, and model parameters of the edge detection model are updated.
6. A method of training a safety production model according to any of claims 1-3, wherein the model architecture of the edge detection model is smaller than the center detection model.
7. A safety production detection method, characterized by being applied to a safety production model trained by the safety production model training method according to any one of claims 1 to 6.
8. A safety production model training apparatus, wherein the safety production model comprises a plurality of edge detection models and a center detection model, the apparatus comprising:
the image acquisition module is used for acquiring the image of the production site acquired by the image acquisition equipment;
the first detection module is used for inputting the image into a corresponding edge detection model for processing to obtain an edge model detection result;
the second detection module is used for inputting the image into a center detection model for processing when the edge model detection result is that the potential safety hazard exists, so as to obtain a center model detection result;
the alarm prompt module is used for sending out a safety alarm prompt when the detection result of the central model is that the potential safety hazard exists;
the actual result acquisition module is used for acquiring an on-site actual safety result when the center model detection result is that the potential safety hazard does not exist;
the first updating training module is used for updating and training the center detection model by taking the image as a training sample when the on-site actual safety result is that potential safety hazards exist;
and the second updating training module is used for updating and training the edge detection model by taking the image as a training sample when the on-site actual safety result is that no potential safety hazard exists.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the safe production model training method of any of claims 1-6.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are for implementing the safe production model training method of any of claims 1-6.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310833679.1A CN116778276A (en) | 2023-07-07 | 2023-07-07 | Safe production model training method, application method, device, equipment and medium |
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| CN202310833679.1A CN116778276A (en) | 2023-07-07 | 2023-07-07 | Safe production model training method, application method, device, equipment and medium |
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