Image processing method for enhancing image recognition

Abstract

The invention discloses an image processing method for realizing enhanced image recognition. A data acquisition and preprocessing module, a feature extraction and optimization module, a parallel processing and scheduling module, a real-time optimization and feedback module, a hardware acceleration integration module and a result output and post-processing module are included. The data acquisition and preprocessing module captures original image data in real time by various sensors or cameras, and executes preprocessing steps of preliminary denoising, color correction, size adjustment and the like so as to reduce the subsequent processing burden; the feature extraction and optimization module performs feature extraction on the preprocessed image by using a deep learning model (such as a convolutional neural network CNN), and reduces calculation redundancy through algorithm optimization; the parallel processing and scheduling module distributes feature extraction tasks to a plurality of processor cores or utilizes a GPU to carry out parallel computing, task distribution is optimized through an intelligent scheduling algorithm, and the overall processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and in particular to an image processing method for realizing enhanced image recognition. Background Art

[0002] Image recognition focuses on the improvement and optimization of image recognition technology, especially the accurate and efficient recognition of target objects in images in complex and changing environments. This method is not only the inheritance and development of traditional image processing algorithms, but also a new challenge and new solution for real-time, accuracy and robustness. It is a crucial and specific thing in the field of computer vision.

[0003] Although image recognition technology has made significant progress, it still faces many challenges in real-time applications. Currently, many image recognition systems are often limited in processing speed due to high computational complexity when processing high-resolution, high-frame-rate video streams, making it difficult to meet real-time requirements. In addition, complex environmental factors such as lighting changes, occlusion, and motion blur often affect recognition accuracy, making the system unstable in practical applications.

[0004] These shortcomings often arise from the complex feature extraction and matching algorithms in the image recognition process. Although these algorithms can effectively capture the details of the image, they also consume a lot of computing resources. When faced with massive amounts of data, the traditional serial processing method is unable to cope with it, resulting in a significant decrease in processing speed. At the same time, the variability of environmental factors further increases the difficulty of recognition, forcing the system to face more uncertainty and complexity. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an image processing method for realizing enhanced image recognition.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: an image processing method for realizing enhanced image recognition, comprising a data acquisition and preprocessing module, a feature extraction and optimization module, a parallel processing and scheduling module, a real-time optimization and feedback module, a hardware acceleration integration module and a result output and post-processing module;

[0007] The data acquisition and preprocessing module uses various sensors or cameras to capture raw image data in real time and performs preprocessing steps such as preliminary denoising, color correction, and size adjustment to reduce the burden of subsequent processing;

[0008] The feature extraction and optimization module uses a deep learning model (such as a convolutional neural network (CNN)) to extract features from the preprocessed image and reduces computational redundancy through algorithm optimization;

[0009] The parallel processing and scheduling module distributes feature extraction tasks to multiple processor cores or uses GPU for parallel computing, optimizes task allocation through intelligent scheduling algorithms, and improves overall processing efficiency;

[0010] The real-time optimization and feedback module dynamically adjusts the algorithm parameters according to the real-time processing results and system load, and provides real-time feedback on the optimization effect;

[0011] The hardware acceleration integration module integrates hardware accelerators such as GPU and FPGA to accelerate the processing of specific computing-intensive tasks;

[0012] The result output and post-processing module outputs the recognition result and performs necessary post-processing (such as target tracking, filtering, etc.) to improve the stability and reliability of the output result.

[0013] As a further description of the above technical solution:

[0014] The data collection and preprocessing module includes a data collection unit and a data preprocessing unit. The data collection unit is responsible for automatically and efficiently collecting required data from various sources, and the data sources include databases, Web APIs, and real-time data streams;

[0015] The data preprocessing unit receives the raw data from the data acquisition unit, and performs cleaning, conversion and standardization processing to ensure that the data quality meets the requirements of subsequent analysis or processing.

[0016] As a further description of the above technical solution:

[0017] The feature extraction and optimization module includes a feature extraction unit and a feature optimization unit;

[0018] The feature extraction unit is responsible for extracting effective and representative features from the original data, including the original attributes directly derived from the data and the data obtained by performing a series of transformations, combinations or dimensionality reduction processes on the original data;

[0019] The feature optimization unit further optimizes the extracted features based on the feature extraction unit to improve the stability and generalization ability of the features;

[0020] Its feature vector F = CNN(I; W, b), where I is the input image, W is the weight matrix, b is the bias term, and CNN represents the forward propagation process of the convolutional neural network.

[0021] As a further description of the above technical solution:

[0022] The parallel processing and scheduling module includes a task allocation unit;

[0023] The task allocation unit is the brain of the parallel processing and scheduling module, responsible for receiving task requests from the upper layer or the outside, and reasonably allocating tasks to various processing units according to the nature of the tasks, dependencies and the current resource status of the system.

[0024] As a further description of the above technical solution:

[0025] The real-time optimization and feedback module includes an intelligent monitoring unit and a dynamic adjustment unit;

[0026] The intelligent monitoring unit serves as the core monitor in the entire image processing process, analyzing and evaluating various indicators in the image recognition process, such as recognition accuracy, processing speed, resource consumption, etc. The intelligent monitoring unit has a built-in algorithm model that can automatically detect abnormal changes in the image data stream and feed this information back to the dynamic adjustment unit in real time for immediate optimization processing;

[0027] The intelligent monitoring unit also has the ability to self-learn. By continuously accumulating and analyzing historical data, it can gradually identify common recognition difficulties and error patterns, and then take preventive measures in advance in future image recognition tasks to improve overall recognition efficiency and accuracy;

[0028] The dynamic adjustment unit also has adaptive capabilities and can automatically adjust the optimization strategy according to current environmental changes and task requirements.

[0029] As a further description of the above technical solution:

[0030] The hardware acceleration integrated module integrates an advanced image processing unit (IPU) and a dedicated coprocessor. These hardware units are designed for image recognition tasks and can process a large amount of pixel data in parallel to achieve fast feature extraction and matching.

[0031] For different types of image data, the module has built-in multiple preprocessing algorithms, noise suppression and edge enhancement. These preprocessing steps not only improve the image quality, but also provide a clearer and more accurate data source for subsequent feature extraction. The acceleration effect is measured by the acceleration ratio (S = T_CPU / T_GPU, where T_CPU is the CPU processing time and T_GPU is the GPU processing time).

[0032] As a further description of the above technical solution:

[0033] The result output and post-processing module is designed to meet the needs of different application scenarios and supports multiple output formats; the real-time feedback and post-processing mechanism ensures the stability and reliability of the system in complex environments;

[0034] The formula for calculating the feature vector is: F=CNN(I;W,b), where F is the feature vector, I is the input image, W and b are the network weights and bias terms respectively, and CNN represents a series of nonlinear transformations performed on the input image by a neural network composed of structures such as convolutional layers, pooling layers, and fully connected layers.

[0035] The acceleration ratio formula: S = T_CPU / T_GPU directly reflects the effect of hardware acceleration. The larger the S value, the more significant the acceleration effect.

[0036] As a further description of the above technical solution:

[0037] The following steps are involved:

[0038] S1, Data acquisition and preprocessing module Various sensors or cameras capture raw image data in real time, and perform preprocessing steps such as preliminary denoising, color correction, and size adjustment to reduce the subsequent processing burden;

[0039] S2, the feature extraction and optimization module uses a deep learning model (such as a convolutional neural network CNN) to extract features from the preprocessed image and reduces computational redundancy through algorithm optimization;

[0040] S3, parallel processing and scheduling module distributes feature extraction tasks to multiple processor cores or uses GPU for parallel computing, optimizes task allocation through intelligent scheduling algorithm, and improves overall processing efficiency;

[0041] S4, real-time optimization and feedback module dynamically adjusts algorithm parameters according to real-time processing results and system load, and provides real-time feedback on optimization effects;

[0042] S5, hardware acceleration integration module integrates GPU, FPGA and other hardware accelerators to accelerate the processing of specific computing-intensive tasks;

[0043] S6. The result output and post-processing module will output the recognition results and perform necessary post-processing target tracking and filtering to improve the stability and reliability of the output results.

[0044] The present invention has the following beneficial effects:

[0045] In the present invention, the visualization and interaction module introduces advanced user interface design to present complex image processing results to users in an intuitive and easy-to-understand way. The module supports interactive operation, allowing users to adjust parameter settings according to actual needs and observe the impact of different parameters on processing results in real time, thereby enhancing the flexibility of the system and user experience.

[0046] The visualization and interaction module of the present invention is not limited to the display of image recognition results, but also provides a wealth of data analysis tools. Users can use these tools to conduct in-depth mining of recognition results and discover the laws and values ​​behind the data. At the same time, the module also supports a variety of data visualization methods, such as bar charts, pie charts, scatter plots, etc., to meet the analysis needs of different users.

[0047] The present invention constructs an efficient, stable, reliable and easy-to-use image recognition system by introducing advanced image processing technology, intelligent optimization mechanism, hardware acceleration solution, humanized interactive design and security protection mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure is a schematic diagram of the overall process of an image processing method for realizing enhanced image recognition proposed by the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0050] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention; the terms "first", "second", and "third" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance. In addition, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] Reference Figure 1 , an embodiment provided by the present invention:

[0052] Embodiment 1:

[0053] An image processing method for realizing enhanced image recognition, comprising a data acquisition and preprocessing module, a feature extraction and optimization module, a parallel processing and scheduling module, a real-time optimization and feedback module, a hardware acceleration integration module and a result output and post-processing module;

[0054] Data acquisition and preprocessing module Various sensors or cameras capture raw image data in real time and perform preprocessing steps such as preliminary denoising, color correction, and size adjustment to reduce the burden of subsequent processing;

[0055] The feature extraction and optimization module uses deep learning models (such as convolutional neural networks (CNNs)) to extract features from preprocessed images and reduces computational redundancy through algorithm optimization, further improving the speed and accuracy of feature extraction. This efficient feature extraction method provides strong support for subsequent image recognition.

[0056] The parallel processing and scheduling module distributes feature extraction tasks to multiple processor cores or uses GPU for parallel computing, optimizes task allocation through intelligent scheduling algorithms, and improves overall processing efficiency. By optimizing task allocation through intelligent scheduling algorithms, the maximum utilization of computing resources is achieved, significantly improving overall processing efficiency.

[0057] The real-time optimization and feedback module dynamically adjusts the algorithm parameters according to the real-time processing results and system load, and provides real-time feedback on the optimization effect. This real-time optimization mechanism enables the image processing method to flexibly respond to various complex scenarios and changes, ensuring the accuracy and stability of the recognition results.

[0058] The hardware acceleration integration module integrates hardware accelerators such as GPU and FPGA to accelerate the processing of specific computing-intensive tasks. This hardware acceleration method further improves the performance of image processing, allowing large-scale and highly complex image recognition tasks to be completed in a shorter time.

[0059] Through the data acquisition and preprocessing module, the raw image data has undergone preliminary denoising, color correction, and resizing before being sent to the core processing flow. These preprocessing measures not only reduce the computational burden of subsequent processing, but also improve the quality of image data, laying a solid foundation for subsequent feature extraction and recognition.

[0060] Embodiment 2:

[0061] An image processing method for realizing enhanced image recognition, wherein the data acquisition and preprocessing module comprises a data acquisition unit and a data preprocessing unit, wherein the data acquisition unit is responsible for automatically and efficiently collecting required data from various sources, wherein the data sources include databases, Web APIs, and real-time data streams;

[0062] The data preprocessing unit receives the raw data from the data acquisition unit and performs cleaning, conversion and standardization to ensure that the data quality meets the requirements of subsequent analysis or processing.

[0063] The feature extraction and optimization module includes a feature extraction unit and a feature optimization unit;

[0064] The feature extraction unit is responsible for extracting effective and representative features from the original data, including the original attributes directly derived from the data and the data obtained by a series of transformations, combinations or dimensionality reduction processes on the original data;

[0065] The feature optimization unit further optimizes the extracted features based on the feature extraction unit to improve the stability and generalization ability of the features;

[0066] Its feature vector F = CNN (I; W, b), where I is the input image, W is the weight matrix, b is the bias term, and CNN represents the forward propagation process of the convolutional neural network.

[0067] The classification and recognition module is the core of the entire image processing process. It receives the output from the feature extraction and optimization module, that is, the carefully designed and optimized feature vector F. This module is mainly composed of the classifier design and training unit and the recognition and prediction unit.

[0068] The recognition and prediction unit is responsible for applying the trained classifier to new image data to achieve automatic recognition and classification of images. When a new image is input into the system, it first goes through the same preprocessing and feature extraction process to generate the corresponding feature vector F_new. Subsequently, the feature vector is fed into the trained classifier, which quickly and accurately determines the category or content of the image based on its ability to learn and understand features. In order to improve the user experience, the unit may also integrate a real-time feedback mechanism to provide users with instant recognition results and confidence assessments.

[0069] Embodiment three:

[0070] An image processing method for realizing enhanced image recognition, wherein the parallel processing and scheduling module includes a task allocation unit;

[0071] The task allocation unit is the brain of the parallel processing and scheduling module. It is responsible for receiving task requests from the upper layer or the outside, and reasonably allocating tasks to various processing units according to the nature of the tasks, dependencies, and the current resource status of the system.

[0072] The real-time optimization and feedback module includes an intelligent monitoring unit and a dynamic adjustment unit;

[0073] As the core monitor in the entire image processing process, the intelligent monitoring unit analyzes and evaluates various indicators in the image recognition process, such as recognition accuracy, processing speed, resource consumption, etc. The intelligent monitoring unit has a built-in algorithm model that can automatically detect abnormal changes in the image data stream and feed this information back to the dynamic adjustment unit in real time for immediate optimization processing;

[0074] The intelligent monitoring unit also has the ability to self-learn. By continuously accumulating and analyzing historical data, it can gradually identify common recognition difficulties and error patterns, and then take preventive measures in advance in future image recognition tasks to improve overall recognition efficiency and accuracy;

[0075] The dynamic adjustment unit also has adaptive capabilities and can automatically adjust the optimization strategy according to current environmental changes and task requirements.

[0076] Going deeper into our image processing system, the parallel processing and scheduling module also includes a key component - the resource pool manager. This manager is carefully designed to maximize resource utilization efficiency and responsiveness. It monitors the operating status of all processing units, including CPU usage, memory usage, and GPU computing load, to ensure that each resource can be allocated efficiently and fairly.

[0077] The resource pool manager uses an innovative load balancing algorithm that takes into account the priority of the task, the amount of resources required, and the current load of each processing unit. In this way, not only can high-priority tasks be processed first, but also overload of a single processing unit can be effectively avoided, ensuring the stability and efficiency of the overall system. At the same time, when a processing unit is idle, the resource pool manager will quickly schedule pending tasks to fill the idle space to avoid wasting resources.

[0078] In order to further improve the accuracy and robustness of image recognition, we introduced multimodal data fusion technology in the intelligent monitoring unit. This technology allows the system to simultaneously analyze image data from different sources (such as visible light images, infrared images, depth images, etc.) and effectively integrate these different modal information. By training and optimizing multimodal data with deep learning algorithms, the system can learn richer feature representations, thereby maintaining a high recognition accuracy in complex environments.

[0079] Embodiment 4:

[0080] An image processing method for achieving enhanced image recognition. The hardware acceleration integrated module integrates an advanced image processing unit (IPU) and a dedicated coprocessor. These hardware units are designed for image recognition tasks and can process a large amount of pixel data in parallel to achieve fast feature extraction and matching.

[0081] For different types of image data, the module has built-in multiple preprocessing algorithms, noise suppression and edge enhancement. These preprocessing steps not only improve the image quality, but also provide a clearer and more accurate data source for subsequent feature extraction. The acceleration effect is measured by the acceleration ratio (S = T_CPU / T_GPU, where T_CPU is the CPU processing time and T_GPU is the GPU processing time).

[0082] The result output and post-processing modules are designed to meet the needs of different application scenarios and support multiple output formats; the real-time feedback and post-processing mechanisms ensure the stability and reliability of the system in complex environments;

[0083] The formula for calculating the feature vector is: F = CNN (I; W, b), where F is the feature vector, I is the input image, W and b are the network weight and bias terms respectively, and CNN represents a series of nonlinear transformations of the input image by a neural network composed of a convolutional layer, a pooling layer, a fully connected layer, etc.

[0084] The acceleration ratio formula: S = T_CPU / T_GPU directly reflects the effect of hardware acceleration. The larger the S value, the more significant the acceleration effect.

[0085] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An image processing method for realizing enhanced image recognition, characterized in that: It includes data acquisition and preprocessing module, feature extraction and optimization module, parallel processing and scheduling module, real-time optimization and feedback module, hardware acceleration integration module and result output and post-processing module; The data acquisition and preprocessing module uses various sensors or cameras to capture raw image data in real time and performs preprocessing steps such as preliminary denoising, color correction, and size adjustment to reduce the burden of subsequent processing; The feature extraction and optimization module uses a deep learning model (such as a convolutional neural network (CNN)) to extract features from the preprocessed image and reduces computational redundancy through algorithm optimization; The parallel processing and scheduling module distributes feature extraction tasks to multiple processor cores or uses GPU for parallel computing, optimizes task allocation through intelligent scheduling algorithms, and improves overall processing efficiency; The real-time optimization and feedback module dynamically adjusts the algorithm parameters according to the real-time processing results and system load, and provides real-time feedback on the optimization effect; The hardware acceleration integration module integrates hardware accelerators such as GPU and FPGA to accelerate the processing of specific computing-intensive tasks; The result output and post-processing module outputs the recognition result and performs necessary post-processing (such as target tracking, filtering, etc.) to improve the stability and reliability of the output result.

2. The image processing method for realizing enhanced image recognition according to claim 1, characterized in that: The data collection and preprocessing module includes a data collection unit and a data preprocessing unit. The data collection unit is responsible for automatically and efficiently collecting required data from various sources, and the data sources include databases, Web APIs, and real-time data streams; The data preprocessing unit receives the raw data from the data acquisition unit, and performs cleaning, conversion and standardization processing to ensure that the data quality meets the requirements of subsequent analysis or processing.

3. The image processing method for realizing enhanced image recognition according to claim 1, characterized in that: The feature extraction and optimization module includes a feature extraction unit and a feature optimization unit; The feature extraction unit is responsible for extracting effective and representative features from the original data, including the original attributes directly derived from the data and the data obtained by performing a series of transformations, combinations or dimensionality reduction processes on the original data; The feature optimization unit further optimizes the extracted features based on the feature extraction unit to improve the stability and generalization ability of the features; Its feature vector F = CNN(I; W, b), where I is the input image, W is the weight matrix, b is the bias term, and CNN represents the forward propagation process of the convolutional neural network.

4. The image processing method for realizing enhanced image recognition according to claim 1, characterized in that: The parallel processing and scheduling module includes a task allocation unit; The task allocation unit is the brain of the parallel processing and scheduling module, responsible for receiving task requests from the upper layer or the outside, and reasonably allocating tasks to various processing units according to the nature of the tasks, dependencies and the current resource status of the system.

5. The image processing method for realizing enhanced image recognition according to claim 1, characterized in that: The real-time optimization and feedback module includes an intelligent monitoring unit and a dynamic adjustment unit; The intelligent monitoring unit acts as the core monitor in the entire image processing process, analyzing and evaluating various indicators in the image recognition process, such as recognition accuracy, processing speed, resource consumption, etc. The intelligent monitoring unit has a built-in algorithm model that can automatically detect abnormal changes in the image data stream and feed this information back to the dynamic adjustment unit in real time for immediate optimization processing; The intelligent monitoring unit also has the ability to self-learn. By continuously accumulating and analyzing historical data, it can gradually identify common recognition difficulties and error patterns, and then take preventive measures in advance in future image recognition tasks to improve overall recognition efficiency and accuracy; The dynamic adjustment unit also has adaptive capabilities and can automatically adjust the optimization strategy according to current environmental changes and task requirements.

6. The image processing method for realizing enhanced image recognition according to claim 1, characterized in that: The hardware acceleration integrated module integrates an advanced image processing unit (IPU) and a dedicated coprocessor. These hardware units are designed for image recognition tasks and can process a large amount of pixel data in parallel to achieve fast feature extraction and matching. For different types of image data, the module has built-in multiple preprocessing algorithms, noise suppression and edge enhancement. These preprocessing steps not only improve the image quality, but also provide a clearer and more accurate data source for subsequent feature extraction. The acceleration effect is measured by the acceleration ratio (S = T_CPU / T_GPU, where T_CPU is the CPU processing time and T_GPU is the GPU processing time).

7. The image processing method for realizing enhanced image recognition according to claim 1, characterized in that: The result output and post-processing module is designed to meet the needs of different application scenarios and supports multiple output formats; the real-time feedback and post-processing mechanism ensures the stability and reliability of the system in complex environments; The formula for calculating the feature vector is: F = CNN (I; W, b), where F is the feature vector, I is the input image, W and b are the network weight and bias terms respectively, and CNN represents a series of nonlinear transformations of the input image by a neural network composed of a convolutional layer, a pooling layer, a fully connected layer, etc. The acceleration ratio formula: S = T_CPU / T_GPU directly reflects the effect of hardware acceleration. The larger the S value, the more significant the acceleration effect.

8. The image processing method for realizing enhanced image recognition according to claim 1, characterized in that: The following steps are involved: S1, Data acquisition and preprocessing module Various sensors or cameras capture raw image data in real time, and perform preprocessing steps such as preliminary denoising, color correction, and size adjustment to reduce the subsequent processing burden; S2, the feature extraction and optimization module uses a deep learning model (such as a convolutional neural network CNN) to extract features from the preprocessed image and reduces computational redundancy through algorithm optimization; S3, parallel processing and scheduling module distributes feature extraction tasks to multiple processor cores or uses GPU for parallel computing, optimizes task allocation through intelligent scheduling algorithm, and improves overall processing efficiency; S4, real-time optimization and feedback module dynamically adjusts algorithm parameters according to real-time processing results and system load, and provides real-time feedback on optimization effects; S5, hardware acceleration integration module integrates GPU, FPGA and other hardware accelerators to accelerate the processing of specific computing-intensive tasks; S6. The result output and post-processing module will output the recognition results and perform necessary post-processing target tracking and filtering to improve the stability and reliability of the output results.

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