A visual method for evaluating strong impact pressure

Abstract

The present invention discloses a visual strong impact pressure evaluation method, which includes: obtaining image information to be evaluated; analyzing and processing the image information to be evaluated to obtain first image information; analyzing and processing the image information to be evaluated and the first image information to obtain target visual analysis result information.

Description

Technical Field

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

[0002] Faced with the current large variety of personal protective equipment, a wide range of sources, and uneven quality, quantitative evaluation of its practicality, safety, and reliability, especially the quantitative evaluation of the degree of individual damage and injury effects under strong impact environments, is one of the hot topics 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 achieve accurate evaluation of strong impact damage effects and equipment protection effects, 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 thus achieve 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 visual strong impact pressure evaluation method that is conducive to visualizing the equipment protection performance under strong impact, improving the efficiency and accuracy of equipment protection performance analysis, and thus realizing rapid identification and precise quantification of individual protective 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, which includes:

[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, comprising:

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

[0010] A first processing module is used to analyze and process 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 visual 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 visual strong impact pressure evaluation method disclosed in the first aspect of the 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 Schematic diagram of a scenario of a visual strong impact pressure evaluation system provided by an embodiment of the present invention;

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

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

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

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

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

[0024] Figure 7 It is a structural 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 solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0026] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

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

[0028] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that one 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 are not 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 this 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, the corresponding data exist for the computer device to process. 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 type of 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 (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0032] Computer vision (CV) is the science of making machines "see." Specifically, it refers to machine vision, where cameras and computers replace the human eye in identifying and measuring objects, performing further image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, optical character recognition (OCR), video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0033] Unimodal information is data consisting of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can often 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 generally 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 the embodiment of the present application, the large model can 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, Wenxin Yiyan and other large-scale language models, which are not limited in the embodiment of the present application.

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

[0036] See also Figure 1 , Figure 1 A schematic diagram of a scenario of a visual strong impact pressure evaluation system provided in an embodiment of the present application is shown. The visual strong impact pressure evaluation system may include a computer device 100, in which a visual strong impact pressure evaluation device is integrated. 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 quantification of individual protective equipment performance evaluation.

[0041] In the embodiments 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 embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. A cloud server is composed of a large number of computers or network servers based on cloud computing.

[0042] It is understood that the computer device 100 used in the embodiments of the present application can be a device that includes both receiving and transmitting hardware, that is, a device that has receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device that has a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. The specific computer device 100 can be a desktop terminal or a mobile terminal. The computer device 100 can also be 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 fewer computer devices as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the visual 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 visual 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 visual strong impact pressure evaluation system shown is only an example. The visual 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 by the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the visual strong impact pressure evaluation system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.

[0046] This invention discloses a visual high-impact pressure evaluation method that facilitates visualization of equipment protection performance under high-impact conditions, improves the efficiency and accuracy of equipment protection performance analysis, and enables rapid identification and precise quantitative evaluation of individual protective equipment performance. Details are provided below.

[0047] Example 1

[0048] See also Figure 2 , Figure 2 This is a flow chart of a visual 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 cloud server for management, etc., which is not limited in the embodiment of the present invention. 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 embodiment of the present invention does not limit this. Furthermore, this application analyzes and processes the image multiple times to arrange the numerical value 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 a rapid evaluation of the injury effect of the explosion impact threat and an accurate quantitative evaluation of the protective performance of the equipment. The embodiment of the present invention does not limit this.

[0053] In this optional embodiment, as an optional implementation manner, the above-mentioned analysis and processing of 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 on the grayscale image information to obtain filtered image information;

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

[0060] Suppression detection processing is performed 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 2 times the third grayscale coefficient, and 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 filtering process performed on 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0067] In an optional embodiment, the image information to be evaluated and the first image information are analyzed and processed to obtain target visual analysis result information, including:

[0068] Performing image analysis processing on the image information to be evaluated and the first image information using the target processing model to obtain 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 image analysis processing of the image information to be evaluated and the first image information using the target processing model 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 in the embodiment 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 thus realizing rapid identification and accurate quantification of individual protective 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 this 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 expressive power and performance of the model, thereby achieving deep feature extraction and representation, and the embodiments of the present invention do not limit this.

[0076] It should be noted that the model architectures of the first, second, and third feature fusion modules are consistent, and are not limited in this embodiment of the present invention. Furthermore, the feature fusion module is constructed based on a gated convolutional model to filter out useless feature representations while achieving multi-scale fusion of feature information of the image information to be evaluated and the first image information, and is not limited in this embodiment of the present invention.

[0077] It should be noted that the target processing model is trained 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0079] In 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 above-mentioned first convolution unit, second convolution unit, third convolution unit, and fourth convolution unit are all 3×3, with a step size of 2, and the number of channels are 512, 256, 128, and 64 respectively, which are not limited in the embodiment of the present invention.

[0082] It should be noted that the above-mentioned first fusion unit and 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0084] In 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 and sixth convolution units are 1×1 and 3×3, respectively, with a step size of 2, which is not limited in this embodiment of the present invention. Furthermore, the fifth convolution unit performs channel dimensionality reduction on the feature information from 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 this embodiment of the present invention.

[0087] It should be noted that the first sampling unit and the second sampling unit are both constructed based on upsampling operations, which is not limited in the embodiments of the present invention. Furthermore, the 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 embodiments of the present invention. Furthermore, the second sampling unit performs linear interpolation upsampling to output a feature map with the same resolution as the image information to be evaluated, which is not limited in the embodiments 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 this 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 thus realizing rapid identification and accurate quantification of individual protective 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 with a stride of 2, which is not limited in this embodiment of the present invention. The convolution kernels of the eighth, ninth, tenth, eleventh, and twelfth convolution units are 3×3 with a stride of 2, which is not limited in this 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 this 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 this 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 this 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 this 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, which is not limited in the embodiment of the present invention.

[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 thus realizing rapid identification and accurate quantification of individual protective 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 aforementioned recognition processing of the first target image information involves analyzing the image result (the first target image information) through the target processing model to identify the color positions and shapes corresponding to different impact forces on the protective equipment caused by a strong impact, thereby obtaining target image recognition result information, which is not limited in this embodiment of the present invention. Furthermore, the aforementioned steps may be based on U-Net or Mask R-CNN, which is not limited in this embodiment of the present invention.

[0105] It should be noted that the aforementioned visualization of the target image recognition result information involves generating a height cloud map from the recognition result, which is not limited in the present embodiment. For example, the height cloud map can be generated using imshow, pcolormesh, or similar functions, and then a color map can be applied to enhance the visual effect, which is not limited in the present embodiment.

[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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0107] Example 2

[0108] See also Figure 3 , Figure 3 This is a schematic diagram of the 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., and the embodiment of the present invention does not limit this. Figure 3 As shown, the device may include:

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

[0110] A first processing module 202 is configured 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 visual 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 thus realizing rapid identification and accurate quantification of individual protective 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 using the target processing model to obtain 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 visual 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0117] In 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 visual 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0120] In 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 visual 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0123] In 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 visual 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0126] In 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 implementation Figure 3 The described visual 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0129] In another optional embodiment, Figure 3 As shown, the second processing module 203 determines 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 implementation Figure 3 The described visual 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 thus realizing rapid identification and accurate quantification of individual protective equipment performance evaluation.

[0133] Example 3

[0134] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of another visual 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., and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0135] A memory 301 storing executable program code;

[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 of the visual strong impact pressure evaluation method described in the first embodiment.

[0138] Example 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 visual strong impact pressure evaluation method described in the first embodiment.

[0140] Example 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 visual strong impact pressure evaluation method described in the first embodiment.

[0142] The device embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0143] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the above technical solution, in essence, or the portion that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, including 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 programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing 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, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein with 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 various 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; Analyzing and processing the image information to be evaluated and the first image information to obtain target visual analysis result information; The step of analyzing and processing the image information to be evaluated to obtain the first image information includes: The grayscale calculation model is used to calculate and process the image information to be evaluated to obtain grayscale image information; Among them, the grayscale calculation model is: HD=a*R+b*G+c*B; 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 gamma coefficient, the second gamma coefficient, and the third gamma coefficient, respectively; the second gamma coefficient is greater than the first gamma coefficient, the first gamma coefficient is greater than the third gamma coefficient, the second gamma coefficient is greater than 2 times the third gamma coefficient, and the first gamma coefficient, the second gamma coefficient, and the third gamma coefficient are positive numbers not greater than 1 and not less than 0; Performing filtering on the grayscale image information to obtain filtered image information; Performing gradient calculation on the filtered image information to obtain gradient image information; Suppression detection processing is performed on the gradient image information to obtain first image 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, 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, 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, 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, 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, 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; Performing visualization image generation processing on the target image recognition result information 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 to obtain image information to be evaluated; A first processing module is used to analyze and process the image information to be evaluated to obtain first image information; A 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; The step of analyzing and processing the image information to be evaluated to obtain the first image information includes: The grayscale calculation model is used to calculate and process the image information to be evaluated to obtain grayscale image information; Among them, the grayscale calculation model is: HD=a*R+b*G+c*B; 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 gamma coefficient, the second gamma coefficient, and the third gamma coefficient, respectively; the second gamma coefficient is greater than the first gamma coefficient, the first gamma coefficient is greater than the third gamma coefficient, the second gamma coefficient is greater than 2 times the third gamma coefficient, and the first gamma coefficient, the second gamma coefficient, and the third gamma coefficient are positive numbers not greater than 1 and not less than 0; Performing filtering on the grayscale image information to obtain filtered image information; Performing gradient calculation on the filtered image information to obtain gradient image information; Suppression detection processing is performed on the gradient image information to obtain first image 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 visual strong impact pressure evaluation method according to any one of claims 1 to 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 visual strong impact pressure evaluation method according to any one of claims 1 to 7.

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