Artificial intelligence image processing method and system

Abstract

The invention belongs to the technical field of artificial intelligence image processing, and particularly relates to an artificial intelligence image processing method and system, and the method comprises the steps: S1, question definition and target setting, S2, data preparation and preprocessing, S3, model selection and design, S4, model training, and S5, subsequent monitoring and maintenance, and saves labor time and energy. According to the method and the system, the AI is used as a model, real-time or near-real-time image analysis is realized by means of an efficient deep learning algorithm and hardware acceleration, meanwhile, through continuous training and feedback, the AI can gradually optimize own performance, incremental learning or transfer learning is used, the AI can adapt to new data and new environmental conditions, and the capability of solving unknown problems is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence image processing, and specifically provides an artificial intelligence image processing method and system. Background Art

[0002] Currently, the so-called artificial intelligence technology is to simulate human consciousness and thinking processes and embody them by technical means. Artificial intelligence is the product of the change and development of thinking beyond ability. With the development of artificial intelligence technology, in aspects such as system establishment, system control, and system decision-making in the computer field, artificial intelligence technology has in-depth applications and plays an important role. Artificial intelligence technology has developed from the initial intelligent simulation to the subsequent intelligent extension and now to intelligent expansion. It has a large number of applications in various fields of people's current lives. Image processing technology is one of the core technologies of the development of artificial intelligence;

[0003] An image is the basis of human vision, an objective reflection of natural scenery, and an important source for humans to understand the world and themselves. What people call an image is the impression or understanding formed in the human brain through the distribution of light reflected or transmitted by an object in the human visual system. When processing images in traditional technologies, relying solely on manpower cannot fully understand the image data of the images, and cannot arrange the images in an orderly manner for sequential processing. Moreover, it is impossible to effectively select an image processing tool that matches the image data to be processed to process the images, and it is impossible to fully utilize the processed images, which is likely to cause waste of resources. Therefore, there is an urgent need to propose an artificial intelligence image processing method and system. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] Therefore, the purpose of the present invention is to provide an artificial intelligence image processing method and system, which saves human time and energy, and realizes real-time or near-real-time image analysis by means of efficient deep learning algorithms and hardware acceleration. At the same time, through continuous training and feedback, the AI can gradually optimize its performance. Using incremental learning or transfer learning, the AI can adapt to new data and new environmental conditions and improve its ability to solve unknown problems;

[0006] To solve the above technical problems, according to one aspect of the present invention, the following technical solutions are provided:

[0007] An artificial intelligence image processing method, which includes the following steps:

[0008] S1, Problem Definition and Goal Setting;

[0009] S2, Data Preparation and Preprocessing;

[0010] S3, Model Selection and Design;

[0011] S4, Model Training;

[0012] S5, Subsequent Monitoring and Maintenance.

[0013] As a preferred solution of an artificial intelligence image processing method described in the present invention, wherein: the step S1 includes:

[0014] S101, Task Clarification: Determine the type of image processing task;

[0015] S102, Definition of Evaluation Criteria: Set the evaluation criteria for the system for subsequent performance evaluation.

[0016] As a preferred solution of an artificial intelligence image processing method described in the present invention, wherein: the step S2 includes:

[0017] S201, Data Acquisition: Obtain a large-scale image data set related to the task, and the data set comes from a public database or is collected customarily;

[0018] S202, Data Cleaning and Preprocessing:

[0019] Remove the noise in the image, ensure the quality of the input image, and unify the image size to meet the input requirements of the neural network;

[0020] Normalize or standardize the image pixel values, usually scale the pixel values to the range of [0, 1] or [-1, 1].

[0021] Data Augmentation: Generate more diverse image samples by rotating, flipping, scaling, and cropping to improve the generalization ability of the model.

[0022] As a preferred solution of an artificial intelligence image processing method described in the present invention, wherein: the step S3 includes:

[0023] S301, Select a suitable model architecture;

[0024] S302, Network Architecture Design: Design a suitable neural network hierarchical structure according to the complexity of the task, and select convolutional layers, pooling layers, and fully connected layers.

[0025] As a preferred solution of an artificial intelligence image processing method described in the present invention, wherein: the step S4 includes:

[0026] S401. Loss function design: Select a loss function suitable for the task, use the labeled training data, train the network through the backpropagation algorithm, and update the network weights;

[0027] S402. Divide the data into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used for parameter tuning and avoiding overfitting, and the test set is used for the final evaluation of the model performance;

[0028] S403. Hyperparameter tuning: Adjust hyperparameters such as the learning rate, batch size, and number of network layers to obtain the best training effect.

[0029] As a preferred solution of an artificial intelligence image processing method according to the present invention, wherein: the step S5 includes:

[0030] S501. Real-time feedback and update: After deployment, update and retrain the model according to the feedback data in actual use to adapt to the changing environment or requirements;

[0031] S502. Performance monitoring: Regularly evaluate the model, check its accuracy, stability, and speed to ensure that it always maintains good performance in actual applications;

[0032] S503. Incremental learning: As new data is continuously generated, the model is continuously improved through incremental learning without having to retrain the entire model.

[0033] An artificial intelligence image processing system, which includes:

[0034] An image acquisition module, which is responsible for collecting raw image or video data from various sources, such as cameras, sensors, databases, and the Internet;

[0035] A data preprocessing module, which is used to clean and prepare the collected images to adapt to subsequent AI model processing, enhance the image quality, and improve the accuracy and speed of the model;

[0036] A feature extraction module, which is used to extract key feature information from the processed images for analysis by a deep learning model;

[0037] A post-processing module, which is used to process and optimize the results output by the AI model to ensure the usability and accuracy of the final results;

[0038] A data storage and management module, which is used to store and manage the image data, processing results, and model files in the system;

[0039] A model deployment and update module, which is used to deploy the trained model to actual applications and perform regular updates and maintenance.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] 1. Automation and high efficiency:

[0042] Save manual time and effort: AI image processing automatically performs tasks such as image classification, detection, and segmentation, reducing the need for manual intervention and saving a large amount of time and labor;

[0043] Real-time processing: With the help of efficient deep learning algorithms and hardware acceleration, the AI image processing method can achieve real-time or near-real-time image analysis;

[0044] 2. Accuracy and high precision:

[0045] Improve recognition accuracy: By training a deep learning model, subtle features in the image can be recognized, thereby improving the accuracy of the task;

[0046] Reduce human errors: The AI system is data-driven and can process images more consistently and stably, thereby reducing the probability of errors;

[0047] 3. Self-learning and adaptability:

[0048] Model self-optimization: Through continuous training and feedback, AI can gradually optimize its performance. Using incremental learning or transfer learning, AI can adapt to new data and new environmental conditions and improve its ability to solve unknown problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the drawings and specific embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0050] Figure 1 It is a schematic diagram of the steps of the image processing method of the present invention;

[0051] Figure 2 It is a block diagram of the image processing system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made with reference to the drawings.

[0053] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein, and those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0054] Secondly, the present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for ease of explanation, the cross-sectional views showing the device structure will be enlarged locally not in accordance with the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0056] The present invention provides an artificial intelligence image processing method and system. Please refer to Figure 1-2 , which includes the following operating steps:

[0057] S1. Problem definition and goal setting;

[0058] S2. Data preparation and preprocessing;

[0059] S3. Model selection and design;

[0060] S4. Model training;

[0061] S5. Subsequent monitoring and maintenance.

[0062] Step S1 includes:

[0063] S101. Task clarification: Determine the type of the image processing task;

[0064] S102. Definition of evaluation criteria: Set the evaluation criteria for the system to facilitate subsequent performance evaluation.

[0065] Step S2 includes:

[0066] S201. Data acquisition: Obtain a large-scale image data set related to the task. The data set is from a public database or is custom-collected;

[0067] S202. Data cleaning and preprocessing:

[0068] Remove the noise in the image, ensure the quality of the input image, and unify the image size to meet the input requirements of the neural network;

[0069] Normalize or standardize the image pixel values, usually scaling the pixel values to the range of [0, 1] or [-1, 1].

[0070] Data augmentation: Generate more diverse image samples through rotation, flipping, scaling, and cropping to improve the generalization ability of the model.

[0071] Step S3 includes:

[0072] S301. Select a suitable model architecture;

[0073] S302. Network architecture design: Design a suitable neural network hierarchy according to the complexity of the task, and select convolutional layers, pooling layers, and fully connected layers.

[0074] Step S4 includes:

[0075] S401. Loss function design: Select a loss function suitable for the task, use the labeled training data, train the network through the backpropagation algorithm, and update the network weights;

[0076] S402. Divide the data into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used for parameter tuning and avoiding overfitting, and the test set is used for final evaluation of the model performance;

[0077] S403. Hyperparameter tuning: Adjust hyperparameters such as the learning rate, batch size, and number of network layers to obtain the best training effect.

[0078] Step S5 includes:

[0079] S501. Real-time feedback and update: After deployment, update and retrain the model according to the feedback data in actual use to adapt to the changing environment or requirements;

[0080] S502. Performance monitoring: Regularly evaluate the model, check its accuracy, stability, and speed to ensure that it always performs well in actual applications;

[0081] S503. Incremental learning: As new data is continuously generated, the model is continuously improved through incremental learning without having to retrain the entire model.

[0082] An artificial intelligence image processing system, which includes:

[0083] An image acquisition module, which is responsible for acquiring raw image or video data from various sources, such as cameras, sensors, databases, and the Internet;

[0084] A data preprocessing module, which is used to clean and prepare the acquired images to adapt to subsequent AI model processing, enhance the image quality, and improve the accuracy and speed of the model;

[0085] A feature extraction module, which is used to extract key feature information from the processed image for analysis by a deep learning model;

[0086] A post-processing module, which is used to process and optimize the results output by the AI model to ensure the usability and accuracy of the final results;

[0087] A data storage and management module, which is used to store and manage the image data, processing results, and model files in the system;

[0088] A model deployment and update module, which is used to deploy the trained model to actual applications and perform regular updates and maintenance.

[0089] Although the present invention has been described above with reference to the embodiments, various improvements can be made to it and components therein can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention can be combined with each other in any way, and the exhaustive description of these combinations is not given in this specification only for the sake of saving space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed in the text, but includes all technical solutions falling within the scope of the claims.

Claims

1. An artificial intelligence image processing method, characterized in that, It includes the following steps: S1. Problem definition and goal setting; S2. Data preparation and preprocessing; S3. Model selection and design; S4. Model training; S5. Subsequent monitoring and maintenance.

2. The artificial intelligence image processing method according to claim 1, wherein The step S1 includes: S101. Clear the task: Determine the type of the image processing task; S102. Define the evaluation criteria: Set the evaluation criteria of the system for subsequent performance evaluation.

3. An artificial intelligence image processing method according to claim 2, characterized in that, The step S2 includes: S201. Data acquisition: Obtain a large-scale image dataset related to the task. The dataset comes from a public database or is custom-collected; S202. Data cleaning and preprocessing: Remove the noise in the image, ensure the quality of the input image, and unify the image size to meet the input requirements of the neural network; Normalize or standardize the image pixel values, usually scaling the pixel values to the range of [0,1] or [-1,1]; Data augmentation: Generate more diverse image samples by rotating, flipping, scaling, and cropping to improve the generalization ability of the model.

4. An artificial intelligence image processing method according to claim 3, characterized in that The step S3 includes: S301. Select a suitable model architecture; S302. Network architecture design: Design a suitable neural network hierarchical structure according to the complexity of the task, and select convolutional layers, pooling layers, and fully connected layers.

5. An artificial intelligence image processing method according to claim 4, characterized in that The step S4 includes: S401. Loss function design: Select a loss function suitable for the task, use the labeled training data, train the network through the backpropagation algorithm, and update the network weights; S402. Divide the data into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used for parameter tuning and avoiding overfitting, and the test set is used for final evaluation of the model performance; S403. Hyperparameter tuning: Adjust hyperparameters such as the learning rate, batch size, and number of network layers to obtain the best training effect.

6. An artificial intelligence image processing method according to claim 5, characterized in that, The step S5 includes: S501. Real-time feedback and update: After deployment, update and retrain the model according to the feedback data in actual use to adapt to the changing environment or requirements; S502. Performance monitoring: Regularly evaluate the model, check its accuracy, stability, and speed to ensure that it always maintains good performance in actual applications; S503. Incremental learning: As new data is continuously generated, the model is continuously improved through incremental learning without retraining the entire model.

7. An artificial intelligence image processing system, characterized in that, It includes: An image acquisition module, which is responsible for collecting raw image or video data from various sources such as cameras, sensors, databases, and the Internet; A data preprocessing module, which is used to clean and prepare the collected images to adapt to subsequent AI model processing, enhance the image quality, and improve the accuracy and speed of the model; A feature extraction module, which is used to extract key feature information from the processed images for analysis by the deep learning model; A post-processing module, which is used to process and optimize the results output by the AI model to ensure the usability and accuracy of the final results; A data storage and management module, which is used to store and manage the image data, processing results, and model files in the system; A model deployment and update module, which is used to deploy the trained model into actual applications and perform regular updates and maintenance.

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