Visual strong impact pressure evaluation method

Abstract

The invention discloses a visual strong impact pressure evaluation method. The method comprises the following steps: acquiring to-be-evaluated image information; analyzing and processing the to-be-evaluated image information to obtain first image information; and analyzing and processing the to-be-evaluated image information and the first image information to obtain target visual analysis result information.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to a visualized strong impact pressure evaluation method. Background Art

[0002] Faced with the current large variety of personal protective equipment, wide sources and uneven quality, quantitative evaluation of its practicality, safety and reliability, especially quantitative evaluation of individual injury degree and injury effect under strong impact environment is one of the hot spots in current protective equipment technology research. At present, my country's research on protection against strong impact threats is still in its infancy, and there is no mature physical model and evaluation method for strong shock wave protection evaluation. There is an urgent need for a means of evaluating the performance of personal protective equipment that can be quickly identified and accurately quantified, so as to realize the accurate evaluation function of strong impact damage effect and equipment protection effect, and guide the replacement and upgrading of personal protective equipment. Therefore, a visual strong impact pressure evaluation method and device are provided to visualize the equipment protection performance under strong impact, improve the efficiency and accuracy of equipment protection performance analysis, and then realize the rapid identification and accurate quantification of personal protective equipment performance evaluation. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a visualized strong impact pressure evaluation method which is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and precise quantification of individual protection equipment performance evaluation.

[0004] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a visual strong impact pressure evaluation method, the method comprising:

[0005] Obtaining image information to be evaluated;

[0006] Analyzing and processing the image information to be evaluated to obtain first image information;

[0007] The image information to be evaluated and the first image information are analyzed and processed to obtain target visual analysis result information.

[0008] A second aspect of an embodiment of the present invention discloses a visual strong impact pressure evaluation device, the device comprising:

[0009] An acquisition module, used for acquiring image information to be evaluated;

[0010] A first processing module, used for analyzing and processing the image information to be evaluated to obtain first image information;

[0011] The second processing module is used to analyze and process the image information to be evaluated and the first image information to obtain target visual analysis result information.

[0012] The third aspect of the present invention discloses another visual strong impact pressure evaluation device, the device comprising:

[0013] A memory storing executable program code;

[0014] a processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the visualized strong impact pressure evaluation method disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the visualized strong impact pressure evaluation method disclosed in the first aspect of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a scene schematic diagram of a visualized strong impact pressure evaluation system provided by an embodiment of the present invention;

[0019] Figure 2 It is a flow chart of a visualized strong impact pressure evaluation method disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural schematic diagram of a visualized strong impact pressure evaluation device disclosed in an embodiment of the present invention;

[0021] Figure 4 It is a structural schematic diagram of another visualized strong impact pressure evaluation device disclosed in an embodiment of the present invention;

[0022] Figure 5 is a structural schematic diagram of a target processing model disclosed in an embodiment of the present invention;

[0023] Figure 6 is a structural schematic diagram of a feature decoding module disclosed in an embodiment of the present invention;

[0024] Figure 7 It is a structural schematic diagram of a feature extraction module disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0027] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0029] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0030] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0031] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0032] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to machine vision such as using cameras and computers to replace human eyes to identify and measure targets, and further perform graphic processing so that computer processing becomes an image that is more suitable for human eye observation or transmission to instrument detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0033] Unimodal information is data of only one type, such as text, image, audio, video, electromagnetic signal, etc. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved on the task.

[0034] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are often used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In an embodiment of the present application, the large model may be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Qianyi Tongwen model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, and Wenxin Yiyan scale language models, which are not limited in the embodiments of the present application.

[0035] The embodiments of the present application provide a visual strong impact pressure evaluation method, device, computer equipment and computer-readable storage medium, which are described in detail below.

[0036] See also Figure 1 , Figure 1 A scene diagram of a visual strong impact pressure evaluation system provided in an embodiment of the present application, wherein the visual strong impact pressure evaluation system may include a computer device 100, wherein the computer device 100 is integrated with a visual strong impact pressure evaluation device, such as Figure 1 Computer equipment in.

[0037] In the embodiment of the present application, the computer device 100 is mainly used to obtain image information to be evaluated;

[0038] Analyzing and processing the image information to be evaluated to obtain first image information;

[0039] The image information to be evaluated and the first image information are analyzed and processed to obtain target visual analysis result information.

[0040] It can visualize the protective performance of equipment under strong impact, improve the efficiency and accuracy of equipment protective performance analysis, and thus achieve rapid identification and precise quantitative performance evaluation of individual protective equipment.

[0041] In the embodiment of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0042] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0043] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the visualized strong impact pressure evaluation system can also include one or more other services, which are not limited here.

[0044] In addition, if Figure 1 As shown, the visualized strong impact pressure evaluation system may further include a memory 200 for storing data, such as image data, position information, and the like.

[0045] It should be noted that Figure 1 The scenario diagram of the visualized strong impact pressure evaluation system shown is merely an example. The visualized strong impact pressure evaluation system and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the visualized strong impact pressure evaluation system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0046] The present invention discloses a visualized strong impact pressure evaluation method, which is beneficial to visualize the equipment protection performance under strong impact, improve the efficiency and accuracy of equipment protection performance analysis, and thus realize rapid identification and accurate quantitative evaluation of individual protective equipment performance. Detailed descriptions are given below.

[0047] Embodiment 1

[0048] See also Figure 2 , Figure 2 : is a flow chart of a visualized strong impact pressure evaluation method disclosed in an embodiment of the present invention. Figure 2 The described visual strong impact pressure evaluation method is applied to a management system, such as a local server or a cloud server for management, and the embodiments of the present invention do not limit this. Figure 2 As shown, the visual strong impact pressure evaluation method may include the following operations:

[0049] 101. Obtain information of the image to be evaluated.

[0050] 102. Analyze and process the image information to be evaluated to obtain first image information.

[0051] 103. Analyze and process the image information to be evaluated and the first image information to obtain target visual analysis result information.

[0052] It should be noted that the above-mentioned image information characterization to be evaluated is based on pressure-sensitive paper, and materials such as high weather resistance, visible light absorption and infrared transparent dyes show different color changes in protective equipment when subjected to strong shock wave loads such as different explosions. The embodiments of the present invention do not limit this. Furthermore, this application analyzes and processes the image multiple times to arrange the values ​​of each pixel on the image into data with a clear structure and easy operation, and then visualizes the data to draw a height cloud map (two-dimensional contour map, three-dimensional surface map, etc.), intuitively revealing the distribution characteristics and height differences between different areas in the image, and realizing the rapid evaluation of the injury effect of the explosion impact threat and the accurate quantitative evaluation of the protective performance of the equipment. The embodiments of the present invention do not limit this.

[0053] In this optional embodiment, as an optional implementation manner, the above-mentioned analyzing and processing the image information to be evaluated to obtain the first image information includes:

[0054] The grayscale calculation model is used to calculate and process the image information to be evaluated to obtain grayscale image information;

[0055] Among them, the grayscale calculation model is:

[0056] HD=a*R+b*G+c*B;

[0057] Wherein, HD represents the pixel value of the pixel in the grayscale image information; R, G and B represent the red component, green component and blue component corresponding to the pixel in the image information to be evaluated respectively; a, b and c represent the first grayscale coefficient, the second grayscale coefficient and the third grayscale coefficient respectively;

[0058] Performing filtering processing on the grayscale image information to obtain filtered image information;

[0059] Performing gradient calculation processing on the filtered image information to obtain gradient image information;

[0060] Perform suppression detection processing on the gradient image information to obtain first image information.

[0061] It should be noted that the above-mentioned second grayscale coefficient is greater than the first grayscale coefficient, the first grayscale coefficient is greater than the third grayscale coefficient, the second grayscale coefficient is greater than twice the third grayscale coefficient, the first grayscale coefficient, the second grayscale coefficient and the third grayscale coefficient are positive numbers not greater than 1 and not less than 0, and the embodiments of the present invention do not limit this.

[0062] It should be noted that the above filtering process for the grayscale image information is Gaussian filtering, which is not limited in the embodiment of the present invention.

[0063] It should be noted that the above-mentioned gradient calculation processing of the filtered image information may be performed based on the Sbole operator, which is not limited in the embodiment of the present invention.

[0064] It should be noted that the above-mentioned suppression detection processing of gradient image information includes non-maximum pixel gradient suppression, dual threshold edge detection and weak edge suppression, etc., which is not limited in the embodiment of the present invention.

[0065] It should be noted that the above analysis and processing of the image information to be evaluated is to improve the edge feature analysis of the image so as to facilitate the subsequent model to extract and analyze the edge features, which is not limited in the embodiment of the present invention.

[0066] It can be seen that the implementation of the visualized strong impact pressure evaluation method described in the embodiment of the present invention is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0067] In an optional embodiment, the above-mentioned analysis and processing of the image information to be evaluated and the first image information to obtain the target visual analysis result information includes:

[0068] Performing image analysis processing on the image information to be evaluated and the first image information by using the target processing model to obtain the first target image information;

[0069] Based on the first target image information, target visual analysis result information is determined.

[0070] It should be noted that the above-mentioned use of the target processing model to perform image analysis processing on the image information to be evaluated and the first image information is to use the target processing model to segment the image area caused by the strong impact of the pressure-sensitive paper / transferable dye in the protective equipment for subsequent image analysis, which is not limited to the embodiments of the present invention.

[0071] It can be seen that the implementation of the visualized strong impact pressure evaluation method described in the embodiment of the present invention is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0072] In another optional embodiment, Figure 5 As shown, the target processing model includes a first convolution module, a second convolution module, a third convolution module, a fourth convolution module, a fifth convolution module, a sixth convolution module, a seventh convolution module, a first feature fusion module, a second feature fusion module, a third feature fusion module, a feature extraction module and a feature decoding module; wherein,

[0073] The input end of the first convolution module is configured to receive the first model input information of the target processing model, and the output end of the first convolution module is respectively connected to the input end of the first feature fusion module and the input end of the feature decoding module; the input end of the second convolution module is configured to receive the second model input information of the target processing model, and the output end of the second convolution module is respectively connected to the input end of the first feature fusion module and the input end of the fourth convolution module; the output end of the first feature fusion module is connected to the input end of the third convolution module; the output end of the third convolution module is connected to the input end of the second feature fusion module; the output end of the fourth convolution module is respectively connected to the input end of the second feature fusion module and the input end of the sixth convolution module; the output end of the second feature fusion module is connected to the input end of the fifth convolution module; the output end of the fifth convolution module is connected to the input end of the third feature fusion module; the output end of the sixth convolution module is connected to the input end of the third feature fusion module; the output end of the third feature fusion module is connected to the input end of the seventh convolution module; the output end of the seventh convolution module is connected to the input end of the feature extraction module; the output end of the feature extraction module is connected to the input end of the feature decoding module; the output end of the feature decoding module is configured to output the model output information of the target processing model.

[0074] It should be noted that the first model input information and the second model input information are respectively the image information to be evaluated and the first image information, which is not limited in the embodiment of the present invention.

[0075] It should be noted that the model architectures of the first convolution module, the second convolution module, the third convolution module, the fourth convolution module, the fifth convolution module, the sixth convolution module and the seventh convolution module are consistent, and the embodiments of the present invention do not limit this. Furthermore, the present application gradually deepens the feature extraction through multiple convolution modules to improve the expression ability and performance of the model, thereby realizing the deep extraction and representation of features, and the embodiments of the present invention do not limit this.

[0076] It should be noted that the model architectures of the first feature fusion module, the second feature fusion module, and the third feature fusion module are consistent, and the embodiment of the present invention does not limit this. Furthermore, the feature fusion module is constructed based on a gated convolution model to achieve multi-scale fusion of feature information of the image information to be evaluated and the first image information while filtering useless feature representations, and the embodiment of the present invention does not limit this.

[0077] It should be noted that the target processing model is obtained by training based on the cross entropy loss function, and the evaluation indicators of the model include accuracy, recall rate, precision and F1 score, which are not limited in the embodiment of the present invention.

[0078] It can be seen that the implementation of the visualized strong impact pressure evaluation method described in the embodiment of the present invention is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0079] In yet another optional embodiment, Figure 5 As shown, the first convolution module includes a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a first fusion unit and a second fusion unit; wherein,

[0080] The input end of the first convolution unit and the input end of the first fusion unit are configured to receive the first model input information of the target processing model; the output end of the first convolution unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is respectively connected to the input end of the third convolution unit and the input end of the second fusion unit; the output end of the third convolution unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the second fusion unit; the output end of the second fusion unit is respectively connected to the input end of the first feature fusion module and the input end of the feature decoding module.

[0081] It should be noted that the convolution kernels of the first convolution unit, the second convolution unit, the third convolution unit, and the fourth convolution unit are all 3×3, the step size is 2, and the number of channels are 512, 256, 128, and 64 respectively, which is not limited in the embodiment of the present invention.

[0082] It should be noted that the above-mentioned first fusion unit and the second fusion unit are both constructed based on element-by-element addition operation, which is not limited in the embodiment of the present invention.

[0083] It can be seen that the implementation of the visualized strong impact pressure evaluation method described in the embodiment of the present invention is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0084] In yet another optional embodiment, Figure 6 As shown, the feature decoding module includes a fifth convolution unit, a sixth convolution unit, a first sampling unit, a second sampling unit and a third fusion unit; wherein,

[0085] The input end of the fifth convolution unit is connected to the output end of the first convolution module, and the output end of the fifth convolution unit is connected to the input end of the third fusion unit; the input end of the first sampling unit is connected to the output end of the first convolution module, and the output end of the first sampling unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the sixth convolution unit; the output end of the sixth convolution unit is connected to the input end of the second sampling unit; the output end of the second sampling unit is configured to output model output information of the target processing model.

[0086] It should be noted that the convolution kernels of the fifth convolution unit and the sixth convolution unit are 1×1 and 3×3 respectively, and the step size is 2, which is not limited in the embodiment of the present invention. Furthermore, the fifth convolution unit performs channel dimension reduction on the feature information of the first convolution module to make it consistent with the dimension of the feature information output by the feature extraction model, which is not limited in the embodiment of the present invention.

[0087] It should be noted that the above-mentioned first sampling unit and the second sampling unit are both constructed based on the upsampling operation, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned first sampling unit performs interpolation upsampling on the feature information output by the feature extraction model so that its feature size is consistent with the feature size of the feature information output by the fifth convolution unit, which is not limited in the embodiment of the present invention. Furthermore, the second sampling unit performs linear interpolation upsampling to output a feature map with the same resolution size as the image information to be evaluated, which is not limited in the embodiment of the present invention.

[0088] It should be noted that the third fusion unit is constructed based on a splicing operation, which is not limited in the embodiment of the present invention.

[0089] It should be noted that the feature decoding module reconstructs the extracted image feature information into an image with a resolution consistent with the image information to be evaluated, which is not limited in the embodiment of the present invention.

[0090] It can be seen that the implementation of the visualized strong impact pressure evaluation method described in the embodiment of the present invention is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0091] In an optional embodiment, if Figure 7 As shown, the feature extraction module includes a seventh convolution unit, an eighth convolution unit, a ninth convolution unit, a tenth convolution unit, an eleventh convolution unit, a twelfth convolution unit, a first normalization unit, a second normalization unit, a third normalization unit, a fourth normalization unit, a fifth normalization unit, a sixth normalization unit, a first activation unit, a second activation unit, a third activation unit, a fourth activation unit, a fifth activation unit, a sixth activation unit, a first pooling unit, a fourth fusion unit and a regularization unit; wherein,

[0092] The input end of the seventh convolution unit, the input end of the eighth convolution unit, the input end of the ninth convolution unit, the input end of the tenth convolution unit and the input end of the first pooling unit are all connected to the output end of the seventh convolution module; the output end of the seventh convolution unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the fourth fusion unit; the output end of the eighth convolution unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the second activation unit; the output end of the second activation unit is connected to the input end of the fourth fusion unit; the output end of the ninth convolution unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the fourth fusion unit; The output end of the tenth convolution unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the fourth activation unit; the output end of the fourth activation unit is connected to the input end of the fourth fusion unit; the output end of the first pooling unit is connected to the input end of the eleventh convolution unit; the output end of the eleventh convolution unit is connected to the input end of the fifth normalization unit; the output end of the fifth normalization unit is connected to the input end of the fifth activation unit; the output end of the fifth activation unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is connected to the input end of the twelfth convolution unit; the output end of the twelfth convolution unit is connected to the input end of the sixth normalization unit; the output end of the sixth normalization unit is connected to the input end of the sixth activation unit; the output end of the sixth activation unit is connected to the input end of the regularization unit; the output end of the regularization unit is connected to the input end of the feature decoding module.

[0093] It should be noted that the convolution kernel of the seventh convolution unit is 1×1, and the step length is 2, which is not limited in the embodiment of the present invention. The convolution kernels of the eighth convolution unit, the ninth convolution unit, the tenth convolution unit, the eleventh convolution unit, and the twelfth convolution unit are 3×3, and the step length is 2, which is not limited in the embodiment of the present invention.

[0094] It should be noted that the above-mentioned first normalization unit, second normalization unit, third normalization unit, fourth normalization unit, fifth normalization unit, and sixth normalization unit are constructed based on the batch normalization layer, which is not limited in the embodiment of the present invention.

[0095] It should be noted that the above-mentioned first activation unit, second activation unit, third activation unit, fourth activation unit, fifth activation unit, and sixth activation unit are constructed based on the RELU activation function, which is not limited in the embodiment of the present invention.

[0096] It should be noted that the above-mentioned first pooling unit is constructed based on the maximum pooling layer, which is not limited in the embodiment of the present invention.

[0097] It should be noted that the fourth fusion unit is constructed based on a splicing operation, which is not limited in the embodiment of the present invention.

[0098] It should be noted that the above regularization unit is constructed based on the Dropout layer, which is not limited in the embodiment of the present invention.

[0099] It should be noted that the above-mentioned feature extraction module mainly improves the receptive field size of the model while maintaining a resolution consistent with the image information to be evaluated, so that the model can better extract the context information of the image, and the embodiment of the present invention does not limit this.

[0100] It can be seen that the implementation of the visualized strong impact pressure evaluation method described in the embodiment of the present invention is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0101] In another optional embodiment, determining target visual analysis result information based on the first target image information includes:

[0102] Performing recognition processing on the first target image information to obtain target image recognition result information;

[0103] The target image recognition result information is processed by visual image generation to obtain the target visual analysis result information.

[0104] It should be noted that the above recognition processing of the first target image information analyzes the image result (first target image information) through the target processing model, identifies the position and shape of the color corresponding to the strong impact on the protective equipment under different impact forces, so as to obtain the target image recognition result information, which is not limited in the embodiment of the present invention. Further, the above steps can be based on U-Net or Mask R-CNN, which is not limited in the embodiment of the present invention.

[0105] It should be noted that the above-mentioned processing of generating a visual image of the target image recognition result information is to generate a height cloud map of the recognition result, which is not limited in the embodiment of the present invention. For example, imshow, pcolormesh or similar functions can be used to generate a height cloud map, and then color mapping (colormap) can be applied to enhance the visual effect, which is not limited in the embodiment of the present invention.

[0106] It can be seen that the implementation of the visualized strong impact pressure evaluation method described in the embodiment of the present invention is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0107] Embodiment 2

[0108] See also Figure 3 , Figure 3 : is a schematic diagram of a structure of a visual strong impact pressure evaluation device disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the device may include:

[0109] An acquisition module 201 is used to acquire image information to be evaluated;

[0110] The first processing module 202 is used to analyze and process the image information to be evaluated to obtain first image information;

[0111] The second processing module 203 is used to analyze and process the image information to be evaluated and the first image information to obtain target visual analysis result information.

[0112] It can be seen that implementation Figure 3 The described visualized strong impact pressure evaluation device is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0113] In another optional embodiment, Figure 3As shown, the second processing module 203 analyzes and processes the image information to be evaluated and the first image information to obtain target visual analysis result information, including:

[0114] Performing image analysis processing on the image information to be evaluated and the first image information by using the target processing model to obtain the first target image information;

[0115] Based on the first target image information, target visual analysis result information is determined.

[0116] It can be seen that implementation Figure 3 The described visualized strong impact pressure evaluation device is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0117] In yet another optional embodiment, Figure 3 As shown, the target processing model includes a first convolution module, a second convolution module, a third convolution module, a fourth convolution module, a fifth convolution module, a sixth convolution module, a seventh convolution module, a first feature fusion module, a second feature fusion module, a third feature fusion module, a feature extraction module and a feature decoding module; wherein,

[0118] The input end of the first convolution module is configured to receive the first model input information of the target processing model, and the output end of the first convolution module is respectively connected to the input end of the first feature fusion module and the input end of the feature decoding module; the input end of the second convolution module is configured to receive the second model input information of the target processing model, and the output end of the second convolution module is respectively connected to the input end of the first feature fusion module and the input end of the fourth convolution module; the output end of the first feature fusion module is connected to the input end of the third convolution module; the output end of the third convolution module is connected to the input end of the second feature fusion module; the output end of the fourth convolution module is respectively connected to the input end of the second feature fusion module and the input end of the sixth convolution module; the output end of the second feature fusion module is connected to the input end of the fifth convolution module; the output end of the fifth convolution module is connected to the input end of the third feature fusion module; the output end of the sixth convolution module is connected to the input end of the third feature fusion module; the output end of the third feature fusion module is connected to the input end of the seventh convolution module; the output end of the seventh convolution module is connected to the input end of the feature extraction module; the output end of the feature extraction module is connected to the input end of the feature decoding module; the output end of the feature decoding module is configured to output the model output information of the target processing model.

[0119] It can be seen that implementation Figure 3 The described visualized strong impact pressure evaluation device is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0120] In yet another optional embodiment, Figure 3 As shown, the first convolution module includes a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a first fusion unit and a second fusion unit; wherein,

[0121] The input end of the first convolution unit and the input end of the first fusion unit are configured to receive the first model input information of the target processing model; the output end of the first convolution unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is respectively connected to the input end of the third convolution unit and the input end of the second fusion unit; the output end of the third convolution unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the second fusion unit; the output end of the second fusion unit is respectively connected to the input end of the first feature fusion module and the input end of the feature decoding module.

[0122] It can be seen that implementation Figure 3 The described visualized strong impact pressure evaluation device is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0123] In yet another optional embodiment, Figure 3 As shown, the feature decoding module includes a fifth convolution unit, a sixth convolution unit, a first sampling unit, a second sampling unit and a third fusion unit; wherein,

[0124] The input end of the fifth convolution unit is connected to the output end of the first convolution module, and the output end of the fifth convolution unit is connected to the input end of the third fusion unit; the input end of the first sampling unit is connected to the output end of the first convolution module, and the output end of the first sampling unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the sixth convolution unit; the output end of the sixth convolution unit is connected to the input end of the second sampling unit; the output end of the second sampling unit is configured to output model output information of the target processing model.

[0125] It can be seen that implementation Figure 3 The described visualized strong impact pressure evaluation device is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0126] In yet another optional embodiment, Figure 3As shown, the feature extraction module includes a seventh convolution unit, an eighth convolution unit, a ninth convolution unit, a tenth convolution unit, an eleventh convolution unit, a twelfth convolution unit, a first normalization unit, a second normalization unit, a third normalization unit, a fourth normalization unit, a fifth normalization unit, a sixth normalization unit, a first activation unit, a second activation unit, a third activation unit, a fourth activation unit, a fifth activation unit, a sixth activation unit, a first pooling unit, a fourth fusion unit and a regularization unit; wherein,

[0127] The input end of the seventh convolution unit, the input end of the eighth convolution unit, the input end of the ninth convolution unit, the input end of the tenth convolution unit and the input end of the first pooling unit are all connected to the output end of the seventh convolution module; the output end of the seventh convolution unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the fourth fusion unit; the output end of the eighth convolution unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the second activation unit; the output end of the second activation unit is connected to the input end of the fourth fusion unit; the output end of the ninth convolution unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the fourth fusion unit; The output end of the tenth convolution unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the fourth activation unit; the output end of the fourth activation unit is connected to the input end of the fourth fusion unit; the output end of the first pooling unit is connected to the input end of the eleventh convolution unit; the output end of the eleventh convolution unit is connected to the input end of the fifth normalization unit; the output end of the fifth normalization unit is connected to the input end of the fifth activation unit; the output end of the fifth activation unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is connected to the input end of the twelfth convolution unit; the output end of the twelfth convolution unit is connected to the input end of the sixth normalization unit; the output end of the sixth normalization unit is connected to the input end of the sixth activation unit; the output end of the sixth activation unit is connected to the input end of the regularization unit; the output end of the regularization unit is connected to the input end of the feature decoding module.

[0128] It can be seen that the implementation Figure 3 The described visualized strong impact pressure evaluation device is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0129] In yet another optional embodiment, Figure 3 As shown, the second processing module 203 determines the target visual analysis result information based on the first target image information, including:

[0130] Performing recognition processing on the first target image information to obtain target image recognition result information;

[0131] The target image recognition result information is processed by visual image generation to obtain the target visual analysis result information.

[0132] It can be seen that the implementation Figure 3 The described visualized strong impact pressure evaluation device is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and further realizing rapid identification and accurate quantification of individual protection equipment performance evaluation.

[0133] Embodiment 3

[0134] See also Figure 4 , Figure 4 It is a structural schematic diagram of another visualized strong impact pressure evaluation device disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 4 As shown, the device may include:

[0135] A memory 301 storing executable program codes;

[0136] a processor 302 coupled to the memory 301;

[0137] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the visualized strong impact pressure evaluation method described in the first embodiment.

[0138] Embodiment 4

[0139] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the visualized strong impact pressure evaluation method described in the first embodiment.

[0140] Embodiment 5

[0141] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the visualized strong impact pressure evaluation method described in the first embodiment.

[0142] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0143] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0144] Finally, it should be noted that the visual strong impact pressure evaluation method disclosed in the embodiment of the present invention only discloses a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions recorded in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A visual strong impact pressure evaluation method, characterized in that: The method comprises: Obtaining image information to be evaluated; Analyzing and processing the image information to be evaluated to obtain first image information; The image information to be evaluated and the first image information are analyzed and processed to obtain target visual analysis result information.

2. The visual strong impact pressure evaluation method according to claim 1, characterized in that: The analyzing and processing the image information to be evaluated and the first image information to obtain target visual analysis result information includes: Performing image analysis processing on the image information to be evaluated and the first image information using a target processing model to obtain first target image information; Based on the first target image information, target visual analysis result information is determined.

3. The visual strong impact pressure evaluation method according to claim 2 is characterized in that: The target processing model includes a first convolution module, a second convolution module, a third convolution module, a fourth convolution module, a fifth convolution module, a sixth convolution module, a seventh convolution module, a first feature fusion module, a second feature fusion module, a third feature fusion module, a feature extraction module and a feature decoding module; wherein, The input end of the first convolution module is configured to receive the first model input information of the target processing model, and the output end of the first convolution module is respectively connected to the input end of the first feature fusion module and the input end of the feature decoding module; the input end of the second convolution module is configured to receive the second model input information of the target processing model, and the output end of the second convolution module is respectively connected to the input end of the first feature fusion module and the input end of the fourth convolution module; the output end of the first feature fusion module is connected to the input end of the third convolution module; the output end of the third convolution module is connected to the input end of the second feature fusion module; the output end of the fourth convolution module is respectively connected to The input end of the second feature fusion module and the input end of the sixth convolution module are connected; the output end of the second feature fusion module is connected to the input end of the fifth convolution module; the output end of the fifth convolution module is connected to the input end of the third feature fusion module; the output end of the sixth convolution module is connected to the input end of the third feature fusion module; the output end of the third feature fusion module is connected to the input end of the seventh convolution module; the output end of the seventh convolution module is connected to the input end of the feature extraction module; the output end of the feature extraction module is connected to the input end of the feature decoding module; the output end of the feature decoding module is configured to output the model output information of the target processing model.

4. The visual strong impact pressure evaluation method according to claim 3 is characterized in that: The first convolution module includes a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a first fusion unit and a second fusion unit; wherein, The input end of the first convolution unit and the input end of the first fusion unit are configured to receive the first model input information of the target processing model; the output end of the first convolution unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is respectively connected to the input end of the third convolution unit and the input end of the second fusion unit; the output end of the third convolution unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the second fusion unit; the output end of the second fusion unit is respectively connected to the input end of the first feature fusion module and the input end of the feature decoding module.

5. The visual strong impact pressure evaluation method according to claim 3 is characterized in that: The feature decoding module includes a fifth convolution unit, a sixth convolution unit, a first sampling unit, a second sampling unit and a third fusion unit; wherein, The input end of the fifth convolution unit is connected to the output end of the first convolution module, and the output end of the fifth convolution unit is connected to the input end of the third fusion unit; the input end of the first sampling unit is connected to the output end of the first convolution module, and the output end of the first sampling unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the sixth convolution unit; the output end of the sixth convolution unit is connected to the input end of the second sampling unit; the output end of the second sampling unit is configured to output the model output information of the target processing model.

6. The visual strong impact pressure evaluation method according to claim 3 is characterized in that: The feature extraction module includes a seventh convolution unit, an eighth convolution unit, a ninth convolution unit, a tenth convolution unit, an eleventh convolution unit, a twelfth convolution unit, a first normalization unit, a second normalization unit, a third normalization unit, a fourth normalization unit, a fifth normalization unit, a sixth normalization unit, a first activation unit, a second activation unit, a third activation unit, a fourth activation unit, a fifth activation unit, a sixth activation unit, a first pooling unit, a fourth fusion unit and a regularization unit; wherein, The input end of the seventh convolution unit, the input end of the eighth convolution unit, the input end of the ninth convolution unit, the input end of the tenth convolution unit and the input end of the first pooling unit are all connected to the output end of the seventh convolution module; the output end of the seventh convolution unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the fourth fusion unit; the output end of the eighth convolution unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the second activation unit; the output end of the second activation unit is connected to the input end of the fourth fusion unit; the output end of the ninth convolution unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the fourth fusion unit; The output end of the tenth convolution unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the fourth activation unit; the output end of the fourth activation unit is connected to the input end of the fourth fusion unit; the output end of the first pooling unit is connected to the input end of the eleventh convolution unit; the output end of the eleventh convolution unit is connected to the input end of the fifth normalization unit; the output end of the fifth normalization unit is connected to the input end of the fifth activation unit; the output end of the fifth activation unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is connected to the input end of the twelfth convolution unit; the output end of the twelfth convolution unit is connected to the input end of the sixth normalization unit; the output end of the sixth normalization unit is connected to the input end of the sixth activation unit; the output end of the sixth activation unit is connected to the input end of the regularization unit; the output end of the regularization unit is connected to the input end of the feature decoding module.

7. The visual strong impact pressure evaluation method according to claim 2 is characterized in that: The determining target visual analysis result information based on the first target image information includes: Performing recognition processing on the first target image information to obtain target image recognition result information; The target image recognition result information is processed for visual image generation to obtain target visual analysis result information.

8. A visual strong impact pressure evaluation device, characterized in that: The device comprises: An acquisition module, used for acquiring image information to be evaluated; A first processing module, used for analyzing and processing the image information to be evaluated to obtain first image information; The second processing module is used to analyze and process the image information to be evaluated and the first image information to obtain target visual analysis result information.

9. A visual strong impact pressure evaluation device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the visualized strong impact pressure evaluation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the visualized strong impact pressure evaluation method according to any one of claims 1 to 7.

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