Classroom environment quality evaluation method and system based on panoramic image

Abstract

The invention provides a classroom environment quality evaluation method and system based on a panoramic image. According to the method, a panoramic image is preprocessed, key feature indexes of a classroom environment are extracted, subjective evaluation of a user is combined, quantitative correlation between classroom environment indexes and environment quality evaluation is established, and a classroom environment quality prediction model is constructed accordingly, so that scientific evaluation of the classroom environment quality based on the panoramic image can be realized; and the evaluation accuracy and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing and environmental assessment, and specifically to a method and system for automatically identifying environmental feature indicators through panoramic images of a classroom, combining the subjective evaluation of users, and evaluating the quality of the classroom environment accordingly, and more particularly to a method and system for evaluating the quality of a classroom environment based on panoramic images. Background Art

[0002] The quality of classroom environment significantly affects students' learning outcomes and experience. Traditional classroom environment quality assessment methods mostly rely on manual evaluation, environmental indicator monitoring or limited sensor data, which have problems such as strong subjectivity and low evaluation efficiency. With the development of image processing technology, panoramic image technology provides a new solution for classroom environment assessment, but existing technologies have not fully utilized the advantages of panoramic images to achieve comprehensive and automatic environmental feature recognition and environmental quality evaluation. Summary of the invention

[0003] The purpose of the present invention is to solve the problems in the prior art and propose a method and system for evaluating the quality of classroom environment based on panoramic images. The method preprocesses the panoramic images, extracts the key characteristic indicators of the classroom environment, combines the subjective evaluation of the users, establishes a quantitative correlation between the classroom environment indicators and the environmental quality evaluation, and constructs a classroom environment quality prediction model based on the panoramic images, which can realize the scientific evaluation of the classroom environment quality based on the panoramic images and improve the accuracy and efficiency of the evaluation.

[0004] The present invention is implemented by the following technical scheme. The present invention proposes a classroom environment quality evaluation method based on panoramic images, and the method comprises the following steps:

[0005] Step 1: Obtain a panoramic image of the classroom to be evaluated;

[0006] Step 2: preprocessing the panoramic image to improve image quality;

[0007] Step 3, extracting classroom environment feature indicators from the preprocessed panoramic image, wherein the feature indicators include space layout features, lighting features, color and material features;

[0008] Step 4: Collect users’ subjective evaluation data on classroom environment quality;

[0009] Step 5: Establishing the quantitative correlation between the environmental feature indicators of image recognition and subjective evaluation;

[0010] Step 6: Based on the quantitative analysis results, a prediction model for classroom environment quality evaluation is established;

[0011] Step seven, use the prediction model to evaluate the environmental quality of the classroom to be evaluated.

[0012] Furthermore, the step 1 is specifically as follows:

[0013] Step 1.1, use a panoramic camera or a common camera combined with image stitching technology to obtain a panoramic image of the classroom;

[0014] Step 1.2: Upload the acquired panoramic image to a server or a local storage device for subsequent processing.

[0015] Furthermore, in step 2, the panoramic image is preprocessed by denoising, adjusting brightness and contrast, and geometric correction; at the same time, the panoramic image is scaled and flipped for data enhancement to increase data diversity and the generalization ability of the model.

[0016] Furthermore, the step three is specifically as follows:

[0017] Step 3.1, the environmental feature index extraction is completed through machine learning or deep learning algorithms, including the identification of the desk and chair placement pattern in the classroom, the density of desks and chairs in the classroom, the window-to-wall / floor ratio, color contrast, color saturation, classroom interface material type and classroom cleanliness;

[0018] Step 3.2: Screen the characteristic indicators obtained in step 3.1 and select the characteristic indicators with significant influence based on preliminary analysis or expert opinions;

[0019] Step 3.3: quantify the characteristic indicators selected in step 3.2, convert them into numerical data, and standardize or normalize them.

[0020] Furthermore, in step five, a multivariate linear regression model is constructed for the quantitative correlation between environmental characteristic indicators and subjective evaluation of environmental quality to quantify the influence of each environmental indicator on the evaluation results.

[0021] Furthermore, the step six is ​​specifically as follows:

[0022] Step 6.1, construct a prediction model based on statistical analysis or machine learning algorithm according to the quantitative analysis results, and predict the subjective evaluation score or grade of the environmental quality of the classroom according to the panoramic image;

[0023] Step 6.2: Based on the prediction model in step 6.1, use an independent test set to evaluate the trained prediction model and calculate performance indicators to ensure the reliability of the prediction model.

[0024] Furthermore, the step seven is specifically as follows:

[0025] Step 7.1, preprocessing and extracting feature indicators of the classroom panoramic image to be evaluated;

[0026] Step 7.2: Input the extracted characteristic indicators into the prediction model to obtain the prediction results of the classroom environment quality and obtain the subjective evaluation score and corresponding grade of the classroom.

[0027] The present invention also proposes a classroom environment quality evaluation system based on panoramic images, the system comprising:

[0028] Panoramic image acquisition module: obtains the panoramic image of the classroom to be evaluated;

[0029] Image preprocessing module: preprocess the panoramic image to improve the image quality;

[0030] Environmental feature index extraction module: extracting classroom environmental feature indexes from the preprocessed panoramic image, the feature indexes including spatial layout features, lighting features, color and material features;

[0031] Subjective evaluation collection module: collects users' subjective evaluation data on classroom environment quality;

[0032] Statistical association establishment module: establishes quantitative associations between environmental feature indicators of image recognition and subjective evaluation;

[0033] Prediction model building module: Based on the quantitative analysis results, a prediction model for classroom environment quality evaluation is established;

[0034] Evaluation and prediction module: Use the prediction model to evaluate the environmental quality of the classroom to be evaluated.

[0035] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for evaluating classroom environment quality based on panoramic images when executing the computer program.

[0036] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for evaluating classroom environment quality based on panoramic images.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention proposes a method and system for evaluating classroom environment quality based on panoramic images. The method can automatically and accurately extract various physical environment characteristics of the classroom by combining panoramic image technology and machine learning or deep learning algorithms, avoiding the limitations of human subjective judgment in traditional evaluation methods, making the evaluation results more scientific and objective. The implementation of the present invention promotes the application and innovation of panoramic image technology, statistical analysis and machine learning technology in the field of education, and provides strong support for building a smart campus and improving the level of educational informatization. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0040] Figure 1 This is a flow chart of a method for evaluating classroom environment quality based on panoramic images described in the present invention. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely 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.

[0042] Combination Figure 1 The present invention proposes a method for evaluating classroom environment quality based on panoramic images, the method comprising the following steps:

[0043] Step 1: Obtain a panoramic image of the classroom to be evaluated;

[0044] The step 1 is specifically as follows:

[0045] Step 1.1, using a panoramic camera, a common camera combined with image stitching technology or other image acquisition methods to obtain a panoramic image of the classroom;

[0046] Step 1.2: Upload the acquired panoramic image to a server or a local storage device for subsequent processing.

[0047] Step 2: preprocessing the panoramic image to improve image quality;

[0048] In step 2, in order to improve the accuracy and efficiency of image recognition, the panoramic image is preprocessed by denoising, adjusting brightness and contrast, and geometric correction. At the same time, the panoramic image is scaled and flipped for data enhancement to increase data diversity and the generalization ability of the model.

[0049] Step 3, extracting classroom environment feature indicators from the preprocessed panoramic image, wherein the feature indicators include space layout features, lighting features, color and material features;

[0050] The step three is specifically as follows:

[0051] Step 3.1, the environmental feature index extraction is completed through machine learning or deep learning algorithms, including the identification of the desk and chair placement pattern in the classroom, the density of desks and chairs in the classroom, the window-to-wall / floor ratio, color contrast, color saturation, classroom interface material type and classroom cleanliness;

[0052] Step 3.2: Screen the feature indicators obtained in step 3.1, and select the feature indicators with significant influence based on preliminary analysis or expert opinions; or use feature selection algorithms to further optimize the feature set to reduce the influence of redundant and noisy features;

[0053] Step 3.3: quantify the characteristic indicators selected in step 3.2, convert them into numerical data, and standardize or normalize them.

[0054] Step 4: Collect users’ subjective evaluation data on classroom environment quality;

[0055] In step 4, users’ overall evaluation results on the quality of classroom environment are collected through questionnaire survey.

[0056] Step 5: Establishing the quantitative correlation between the environmental feature indicators of image recognition and subjective evaluation;

[0057] In step 5, a multivariate linear regression model is constructed based on the quantitative correlation between environmental characteristic indicators and subjective evaluation of environmental quality to quantify the influence of each environmental indicator on the evaluation results. Alternatively, machine learning algorithms (such as random forests, neural networks, etc.) are used for modeling to improve the accuracy and generalization ability of prediction.

[0058] Step 6: Based on the quantitative analysis results, a prediction model for classroom environment quality evaluation is established;

[0059] The step six is ​​specifically as follows:

[0060] Step 6.1, construct a prediction model based on statistical analysis or machine learning algorithm according to the quantitative analysis results, and predict the subjective evaluation score or grade of the environmental quality of the classroom according to the panoramic image;

[0061] Step 6.2: Based on the prediction model in step 6.1, use an independent test set to evaluate the trained prediction model and calculate performance indicators to ensure the reliability of the prediction model.

[0062] Furthermore, the prediction model can be further optimized based on the evaluation results, including adjusting the model structure, adding variables, etc.

[0063] Step seven, use the prediction model to evaluate the environmental quality of the classroom to be evaluated.

[0064] The step seven is specifically as follows:

[0065] Step 7.1, preprocessing and extracting feature indicators of the classroom panoramic image to be evaluated;

[0066] Step 7.2: Input the extracted characteristic indicators into the prediction model to obtain the prediction results of the classroom environment quality and obtain the subjective evaluation score and corresponding grade of the classroom.

[0067] The method may further include step eight, retraining the prediction model to improve its accuracy based on regularly collecting new panoramic images and evaluation data.

[0068] The step eight is specifically as follows:

[0069] Step 8.1: Questionnaires can be distributed to users regularly through the Web or mobile applications to collect new evaluation results of the classroom environment.

[0070] Step 8.2: Integrate the newly collected data with the old data, retrain and verify the prediction model, and improve the accuracy of the model.

[0071] The present invention also proposes a classroom environment quality evaluation system based on panoramic images, the system comprising:

[0072] Panoramic image acquisition module: obtains the panoramic image of the classroom to be evaluated;

[0073] Image preprocessing module: preprocess the panoramic image to improve the image quality;

[0074] Environmental feature index extraction module: extracting classroom environmental feature indexes from the preprocessed panoramic image, the feature indexes including spatial layout features, lighting features, color and material features;

[0075] Subjective evaluation collection module: collects users' subjective evaluation data on classroom environment quality;

[0076] Statistical association establishment module: establishes quantitative associations between environmental feature indicators of image recognition and subjective evaluation;

[0077] Prediction model building module: Based on the quantitative analysis results, a prediction model for classroom environment quality evaluation is established;

[0078] Evaluation and prediction module: Use the prediction model to evaluate the environmental quality of the classroom to be evaluated.

[0079] The system also includes a feedback and update module: based on the regular collection of new panoramic images and evaluation data, the prediction model is retrained to improve its accuracy.

[0080] The present invention has determined a new method flow for evaluating indoor environmental quality in combination with panoramic images. By automatically processing panoramic images and extracting environmental features, and combining the user's subjective evaluation to build an environmental quality prediction model, the objective, accurate and efficient evaluation of classroom environmental quality is achieved. The present invention also realizes the automation and intelligence of the evaluation process. From the acquisition, preprocessing, and environmental feature extraction of panoramic images, to statistical correlation analysis, prediction model construction, and then to evaluation prediction and feedback mechanism, the entire process can be automatically completed with the assistance of a computer system, which greatly improves the evaluation efficiency and reduces the interference of human factors.

[0081] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for evaluating classroom environment quality based on panoramic images when executing the computer program.

[0082] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for evaluating classroom environment quality based on panoramic images.

[0083] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). It should be noted that the memory of the method described in the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0084] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0085] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0086] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined and performed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0087] The above is a detailed introduction to the classroom environment quality evaluation method and system based on panoramic images proposed in the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A classroom environment quality evaluation method based on panoramic images, characterized in that: The method comprises the following steps: Step 1: Obtain a panoramic image of the classroom to be evaluated; Step 2: preprocessing the panoramic image to improve image quality; Step 3, extracting classroom environment feature indicators from the preprocessed panoramic image, wherein the feature indicators include space layout features, lighting features, color and material features; Step 4: Collect users’ subjective evaluation data on classroom environment quality; Step 5: Establishing the quantitative correlation between the environmental feature indicators of image recognition and subjective evaluation; Step 6: Based on the quantitative analysis results, a prediction model for classroom environment quality evaluation is established; Step seven, use the prediction model to evaluate the environmental quality of the classroom to be evaluated.

2. The method according to claim 1, characterized in that: The step 1 is specifically as follows: Step 1.1, use a panoramic camera or a common camera combined with image stitching technology to obtain a panoramic image of the classroom; Step 1.2: Upload the acquired panoramic image to a server or a local storage device for subsequent processing.

3. The method according to claim 2, characterized in that In step 2, the panoramic image is preprocessed by denoising, adjusting brightness and contrast, and geometric correction. At the same time, the panoramic image is scaled and flipped for data enhancement to increase data diversity and the generalization ability of the model.

4. The method according to claim 3, characterized in that The step three is specifically as follows: Step 3.1, the environmental feature index extraction is completed through machine learning or deep learning algorithms, including the identification of the desk and chair placement pattern in the classroom, the density of desks and chairs in the classroom, the window-to-wall / floor ratio, color contrast, color saturation, classroom interface material type and classroom cleanliness; Step 3.2: Screen the characteristic indicators obtained in step 3.1 and select the characteristic indicators with significant influence based on preliminary analysis or expert opinions; Step 3.3: quantify the characteristic indicators selected in step 3.2, convert them into numerical data, and standardize or normalize them.

5. The method according to claim 4, characterized in that In step five, a multivariate linear regression model is constructed based on the quantitative correlation between environmental characteristic indicators and subjective evaluation of environmental quality to quantify the influence of each environmental indicator on the evaluation results.

6. The method according to claim 5, characterized in that The step six is ​​specifically as follows: Step 6.1, construct a prediction model based on statistical analysis or machine learning algorithm according to the quantitative analysis results, and predict the subjective evaluation score or grade of the environmental quality of the classroom according to the panoramic image; Step 6.2: Based on the prediction model in step 6.1, use an independent test set to evaluate the trained prediction model and calculate performance indicators to ensure the reliability of the prediction model.

7. The method according to claim 6, characterized in that The step seven is specifically as follows: Step 7.1, preprocessing and extracting feature indicators of the classroom panoramic image to be evaluated; Step 7.2: Input the extracted characteristic indicators into the prediction model to obtain the prediction results of the classroom environment quality and obtain the subjective evaluation score and corresponding grade of the classroom.

8. A classroom environment quality evaluation system based on panoramic images, characterized in that: The system comprises: Panoramic image acquisition module: obtains the panoramic image of the classroom to be evaluated; Image preprocessing module: preprocess the panoramic image to improve the image quality; Environmental feature index extraction module: extracting classroom environmental feature indexes from the preprocessed panoramic image, the feature indexes including spatial layout features, lighting features, color and material features; Subjective evaluation collection module: collects users' subjective evaluation data on classroom environment quality; Statistical association establishment module: establishes quantitative associations between environmental feature indicators of image recognition and subjective evaluation; Prediction model building module: Based on the quantitative analysis results, a prediction model for classroom environment quality evaluation is established; Evaluation and prediction module: Use the prediction model to evaluate the environmental quality of the classroom to be evaluated.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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