Self-adaptive multi-modal image recognition system and method thereof

Abstract

The invention provides a self-adaptive multi-modal image recognition system and a method thereof, and aims to solve the problem of insufficient recognition accuracy in different illumination conditions and complex backgrounds in the prior art. According to the system, the image quality is optimized through the self-adaptive preprocessing module, the intrinsic features of the image are deeply mined in combination with the multi-modal feature extraction module, and the accuracy of target recognition is improved through the illumination self-adaptive adjustment module and the complex background separation module. By using a multi-scale feature fusion technology and deep learning model training, the system can learn and identify images in various complex environments. And the adaptive learning optimization module dynamically adjusts an identification strategy according to real-time feedback, and ensures efficient operation of the system under a real-time or near-real-time condition. In addition, the multi-algorithm fusion decision module and the performance evaluation and feedback module further improve the recognition accuracy and user satisfaction of the system. The system and the method provided by the invention can be widely applied to multiple fields such as automatic driving, medical imaging, security monitoring and the like.

Description

Technical Field

[0001] The present invention relates to the technical fields of image processing and computer vision, and in particular to an adaptive multimodal image recognition system and method thereof. Background Art

[0002] In the field of image recognition technology, traditional image recognition systems typically rely on fixed lighting conditions and simple background environments to achieve high recognition accuracy. However, in real-world applications, images are often affected by varying lighting conditions, and background environments can be complex and contain multiple interference factors, which severely limit the performance and application scope of traditional image recognition systems.

[0003] With technological advancements, several improved image recognition algorithms have been proposed, aiming to enhance the system's adaptability to changing lighting conditions and complex backgrounds. These algorithms improve image recognition accuracy through preprocessing techniques such as enhancing image contrast, removing noise, and adjusting white balance, as well as employing specific feature extraction and background separation techniques. However, these improvements still have limitations, particularly in dynamically changing lighting conditions and highly complex background environments. The adaptability and recognition accuracy of existing technologies still need to be further improved.

[0004] With the development of deep learning technology, image recognition methods based on deep neural networks have demonstrated powerful feature learning capabilities and recognition potential. However, these methods often require large amounts of labeled data for training and may face challenges in real-time performance, generalization, and interpretability in practical applications. Therefore, combining the advantages of traditional image processing techniques with deep learning to develop an adaptive multimodal image recognition system that can adapt to complex environments while maintaining high accuracy has become a hot topic and a challenge in current research. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive multimodal image recognition system and method capable of highly accurate image recognition in a variety of lighting conditions and complex backgrounds. By integrating multiple innovative modules, this system implements adaptive image preprocessing, multimodal feature extraction, adaptive illumination adjustment, complex background separation, multiscale feature fusion, deep learning model training, adaptive learning optimization, multi-algorithm fusion decision-making, and performance evaluation and feedback, significantly improving the performance and application scope of image recognition.

[0006] The invention is described in detail as follows:

[0007] Preferably, the adaptive preprocessing module ensures automation and optimization of image preprocessing through the following specific steps: First, the module automatically activates the light detection algorithm, which intelligently identifies the current ambient light level by calculating the image's histogram and statistical light distribution. Next, based on the detection results, the denoising algorithm adaptively adjusts its intensity parameters, for example, setting the noise reduction threshold as a function of the ambient light intensity. If the light is weak, the noise reduction threshold is appropriately increased in steps of 0.1 to reduce image noise. Then, the contrast enhancement algorithm adjusts the gamma correction parameters according to the lighting conditions. If the image is dark, the contrast is automatically increased, such as by dynamically adjusting the gamma value within a range of 0.5 to 1.5. In addition, the white balance adjustment algorithm dynamically calculates and corrects the color temperature to adapt to color casts in different lighting environments, ensuring the true restoration of image colors. Finally, the module outputs the optimized image, providing clear, accurate, and high-quality visual information for the next stage of feature extraction and recognition. Through this coherent automated process, the adaptive preprocessing module significantly improves the usability of images and the accuracy of subsequent processing.

[0008] Preferably, the multimodal feature extraction module implements deep learning driven feature extraction through the following specific steps: first, the module receives the input image after adaptive preprocessing, and uses a convolutional neural network (CNN) for initial feature extraction, wherein the CNN is composed of a stack of multiple convolutional layers and pooling layers. The convolutional layer uses a 3x3 convolution kernel with a step size of 1 for feature mapping, and the subsequent pooling layer uses a 2x2 maximum pooling window to reduce the spatial dimension of the feature and increase the invariance to image displacement; then, the features extracted from the high-level layers of the CNN are fed into a recurrent neural network (RNN), especially a long short-term memory network (LSTM), to process Image sequence data captures dynamic temporal features. The LSTM network sets the number of hidden layer nodes to 128 and the time step to 1. Its gating mechanism effectively avoids long-term dependency issues. In addition, the module also includes a modal feature fusion unit that effectively integrates data from different modalities, such as vision, infrared, and radar. Through a weighted fusion strategy, the weights of different modal features are dynamically adjusted. For example, visual features may be assigned a weight of 0.6, while infrared and radar features may each be assigned a weight of 0.2. Finally, the fused feature vector is sent to the fully connected layer for final feature processing before classification, ensuring the richness and adaptability of feature expression. Through this process, the multimodal feature extraction module can fully capture the visual and temporal information in the image, providing strong feature support for subsequent object detection and classification tasks.

[0009] Preferably, the illumination adaptive adjustment module optimizes the image illumination conditions through the following fine-tuning steps: first, the module starts the illumination estimation algorithm, analyzes the brightness and contrast distribution of the image, and evaluates the illumination conditions by calculating the image's histogram equalization and statistics such as the mean, median, and standard deviation; then, based on the evaluation results, the module automatically selects an appropriate illumination correction method, such as histogram equalization or Retinex theory, to simulate a uniform illumination environment; then, an adaptive algorithm is applied to adjust the illumination of the image. For example, if the image is dark, the module will enhance the overall brightness; if there are highlight or shadow areas in the image, the module will perform local contrast adjustment, such as using the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm to enhance the local contrast; in addition, the module also includes an illumination consistency check unit to ensure that the illumination uniformity of the adjusted image in different areas meets the preset standard, for example, ensuring that the brightness variation of the image is within ±10%; finally, the module outputs the illumination-adjusted image, providing visual data with more consistent and optimized illumination conditions for subsequent feature extraction and recognition tasks. Through this coherent and automated process, the adaptive illumination adjustment module significantly improves the robustness and accuracy of image recognition.

[0010] Preferably, the complex background separation module achieves effective separation of the target object from the background through the following specific steps: first, the module starts the background subtraction algorithm to identify and extract moving objects by analyzing the pixel-level differences between consecutive frames, and sets the frame difference threshold parameter to 25 so that when the brightness difference between consecutive frames exceeds the threshold, the pixel is marked as the foreground; then, morphological operations such as erosion and dilation are used to refine the segmentation results, where the erosion operation uses a 3x3 rectangular structure element and the dilation operation uses a 5x5 rectangular structure element to remove noise and fill small holes; then, the module further separates the target object through the foreground segmentation algorithm, using a deep learning-based method such as U-Net or Mask R-CNN, which accurately distinguishes the foreground and background through the trained masks, and the classification loss function weight may be set to 0.5 and the bounding box regression loss function weight may be set to 0.3; in addition, the module also includes a context information integration unit, which uses the context information of the scene to assist the separation process, such as by analyzing the relative position and size relationship between objects to improve the accuracy of segmentation; finally, the module outputs the accurately separated target object, providing a clean, interference-free object image for subsequent feature extraction and recognition tasks. Through this fine-tuned process, the complex background separation module significantly improves the accuracy and robustness of the recognition task.

[0011] Preferably, the multi-scale feature fusion module realizes efficient recognition of targets of different sizes through the following specific steps: first, the module adopts a multi-scale architecture in CNN, and captures features at different levels by stacking convolution kernels of different sizes, for example, using 3x3, 5x5 and 7x7 convolution kernels to extract feature representations from shallow to deep layers; then, using feature pyramid networks (FPN) or U-Net structures, which can capture features at multiple resolutions, the feature maps extracted from the deep network are combined with the high-resolution feature maps of the shallow network through side connections; then, a feature fusion strategy is set, such as For example, a 1x1 convolution kernel is used for feature recalibration, and a 3x3 convolution kernel is used for spatial feature fusion to ensure that features of different scales can be effectively combined. In addition, the module transfers information between different layers through upsampling and downsampling operations. Upsampling uses bilinear interpolation with a scale of 2 to increase the resolution of the feature map, while downsampling uses maximum pooling or average pooling with a step size of 2 to reduce the spatial size of the feature map. Finally, the fused feature map is fed into the subsequent classification and detection network for final target recognition and positioning. The construction of the feature pyramid enhances the system's ability to recognize small and large targets. Through this sophisticated multi-scale processing flow, the multi-scale feature fusion module significantly improves the model's recognition accuracy and robustness for targets of various sizes.

[0012] Preferably, the deep learning model training module achieves efficient training and improved domain adaptability through the following specific steps: First, the module uses a large-scale dataset containing more than 100,000 labeled samples, which cover a variety of categories and environmental conditions to ensure that the model has a wide range of generalization capabilities; then, the cross-extraction loss function L is used. ce To optimize the classification task, the loss function is defined as:

[0013]

[0014] Where N is the number of stars, y o，c is the one-hot encoding of the true label, y p，c is the probability distribution predicted by the model; then, using the transfer learning technology, the model parameters pre-trained on a large dataset (such as ImageNet) are used as a starting point, and fine-tuned on data in a specific field, through the transfer learning loss function L tl To further optimize the model, this function may incorporate new domain-specific features:

[0015] L tl =λL ce +(1-λ)L reg

[0016] Among them, L regis a regularization term used to control model complexity and prevent overfitting. λ is a proportional parameter that balances transfer learning and domain adaptability, and is usually set to 0.5. In addition, the module also uses data enhancement techniques such as random cropping, flipping, and color jittering to further improve the generalization ability of the model. Finally, through early stopping and model checkpointing strategies, training is stopped when no performance improvement is observed on the validation focus, and the best performing model is selected for saving and deployment. Through this process, deep The learning model training module ensures the rapid adaptation and continuous optimization of the model in new fields.

[0017] Preferably, the adaptive learning optimization module first monitors the performance of the model in actual application in real time, collects feedback on key indicators such as accuracy and recall rate, and Data; Based on the feedback data, an adaptive learning rate adjustment algorithm is used, such as the learning rate decay formula η t+1 =η t α, where η t is the learning rate of the tth step, α is a decay factor less than 1 to adapt to the changes in model performance; the model weights are updated using the online speed-up descent method, and the formula is Here t represents the model weight at step t, J(w t ) is the loss function, is the gradient of the loss function with respect to the weight; the module controls the model complexity by introducing the regularization term Ω(w), such as L2 regularization, the formula is J reg (w t )=J(w t )+λ·Ω(w), where λ is the regularization coefficient to prevent overfitting; the module implements the model update strategy, such as using the exponential moving average to smooth the model weights, the formula is w EMA =β·w EMA +(1-β)·w t , where β is a smoothing coefficient, which usually takes a value between 0.9 and 0.999 to achieve stable update of model parameters.

[0018] Preferably, the results include category predictions and corresponding confidence scores; a weighted voting mechanism is used to assign weights to the predictions of each algorithm, and the weights are based on the performance of the algorithm on the validation set. An algorithm with an accuracy of 90% on the validation set may receive a weight of 0.7, while an algorithm with an accuracy of 85% receives a weight of 0.3; the module performs probabilistic fusion of the predictions of each algorithm through a Bayesian method, calculates the posterior probability distribution, and updates the prediction results using the formula P(y|x)∝P(x|y)P(y), where P(y|x) is the posterior probability of category y given the observation x, P(x|y) is the likelihood probability, and P(y) is the prior probability of the category; in addition, the module also considers a confidence threshold. Predictions below a certain threshold (for example, 0.5) will not be counted in the final decision to improve the robustness of the system; finally, the module outputs a final recognition result that combines the advantages of multiple algorithms to ensure high accuracy and robustness even in complex scenarios. Through this meticulous integration process, the multi-algorithm fusion decision module significantly improves the overall recognition performance of the system.

[0019] Preferably, the performance evaluation and feedback module ensures continuous improvement and performance enhancement of the system through the following detailed steps: First, the module uses automated performance monitoring tools to track key performance indicators of the system in real time, such as processing speed, accuracy, resource consumption, etc., and sets thresholds. For example, when the processing speed is lower than 20 milliseconds / frame or the accuracy is lower than 99%, performance evaluation is triggered; then, data visualization technology is used to display performance indicators to facilitate analysis and identification of performance bottlenecks, such as using line graphs to track accuracy trends and bar graphs to compare resource consumption of different algorithms; then, the module starts the user feedback collection mechanism, through questionnaires, user interviews or direct feedback to the system. The module systematically collects user opinions and evaluates user satisfaction and system usage experience. Furthermore, based on performance evaluation and user feedback, the module triggers the algorithm optimization process, including adjusting model parameters, improving data processing strategies, or introducing new technologies, such as adjusting the model's learning rate or increasing the diversity of data augmentation based on feedback. The module then implements A / B testing to compare performance differences before and after optimization to ensure the effectiveness of the optimization measures, such as comparing processing speed and accuracy between the control and experimental groups. Finally, based on test results and further user feedback, the module iteratively updates the algorithm and pushes the optimized algorithm to the production environment through an automated deployment process, achieving closed-loop optimization. Through this consistent evaluation and feedback process, the performance evaluation and feedback module continuously drives system performance optimization and enhances user experience.

[0020] Preferably, the real-time performance assurance module ensures efficient operation of the system under real-time or near-real-time conditions through the following steps: First, the module tracks the response time and processing delay of the algorithm through a real-time monitoring system and sets strict performance indicators, such as a maximum delay of no more than 50 milliseconds, to meet the requirements of real-time processing; Next, the module uses performance analysis tools to identify bottlenecks in the algorithm, such as memory usage peaks or computationally intensive operations, and optimizes these bottlenecks, for example, by reducing processing time through parallel computing or GPU acceleration; Then, the module implements code optimization strategies, including algorithm simplification, loop unrolling, memory access optimization, etc., to reduce the number of CPU cycles required for each frame processing; In addition, the module uses model compression techniques, such as model pruning, quantization, and knowledge distillation, to reduce model size and computational complexity, for example, compressing the model size to 75% of the original size while maintaining at least 95% of the original accuracy; Then, the module dynamically adjusts system resource allocation, reasonably allocating CPU and memory resources based on the current system load and task priority, to ensure that critical tasks have access to sufficient computing resources; Finally, the module implements adaptive load balancing, dynamically adjusting task allocation based on real-time monitoring data to avoid overload and maintain smooth system operation. Through these meticulous steps, the real-time performance assurance module ensures that the system can maintain its ability to respond quickly and process efficiently when faced with large amounts of data or complex tasks.

[0021] The method of the adaptive multimodal image recognition system of the present invention is characterized by the following comprehensive and detailed steps:

[0022] Preliminary analysis of input image: The system first receives the input image and uses the image analysis algorithm to set the lighting condition threshold, such as 50 units, and the background complexity is divided into three levels: low, medium, and high, for preliminary analysis.

[0023] Adaptive preprocessing: Based on the analysis results, the adaptive preprocessing module automatically adjusts the denoising intensity to 80% of the preset threshold, enhances the contrast to 120% of the original contrast, and automatically adjusts the white balance through color temperature detection.

[0024] Multimodal feature extraction: The multimodal feature extraction module extracts the color, texture, and shape features of the image by setting color space conversion parameters, such as HSV to RGB conversion, and uses specific algorithms such as edge detection to extract depth information.

[0025] Adaptive lighting adjustment: The adaptive lighting adjustment module automatically applies histogram equalization or gamma correction according to lighting conditions, setting adjustment parameters such as gamma value from 0.5 to 1.5 to simulate a uniform lighting environment.

[0026] Complex background separation: The complex background separation module uses background subtraction technology, sets the frame difference threshold parameter to 25, and uses morphological operations such as 3x3 structure element dilation and erosion to separate the target object.

[0027] Multi-scale feature fusion: Build a feature pyramid, set the resolution ratios of different levels, such as 1 / 4, 1 / 8, 1 / 16, and 1 / 32 of the original size, and integrate features of different scales through fusion strategies.

[0028] Deep learning model training: A large-scale labeled dataset was used to train the deep learning model, with a batch size of 32 and a learning rate of 0.001. Transfer learning techniques were used for fine-tuning.

[0029] Adaptive learning optimization: Based on the training results, the adaptive learning optimization module dynamically adjusts the learning rate, such as to 0.0005, and applies regularization techniques to prevent overfitting.

[0030] Multi-algorithm fusion decision-making: The trained model is applied to the actual image recognition task. By setting a weighted voting mechanism, such as setting the visual feature weight to 0.6, the recognition results of different algorithms are integrated.

[0031] Performance evaluation and feedback: Collect performance data in actual applications, such as accuracy of no less than 99% and processing speed of less than 20 milliseconds per frame, and continuously evaluate system performance through user feedback.

[0032] System iterative optimization: Based on performance evaluation results and user feedback, iteratively optimize system parameters, such as adjusting model structure or updating data enhancement strategies.

[0033] Real-time performance guarantee: Ensure the system operates efficiently in real-time or near real-time conditions, set the maximum latency threshold to 50 milliseconds, and perform resource scheduling and performance optimization when necessary.

[0034] Through these specific, clear and comprehensive steps, the adaptive multimodal image recognition system of the present invention can adapt to changing application requirements and environmental conditions and achieve efficient and accurate image recognition.

[0035] The system and method of the present invention not only improve the accuracy of image recognition through the collaborative work of the above modules, but also enhance the adaptability and generalization ability of the system, enabling it to operate stably in a variety of practical application scenarios.

[0036] The technical solution of the present invention can bring the following significant technical effects:

[0037] 1. Improved recognition accuracy: By adaptively adjusting image lighting conditions through an adaptive preprocessing module and integrating it with a multimodal feature extraction module to deeply explore the intrinsic characteristics of the image, this invention significantly improves target recognition accuracy under varying lighting conditions and complex backgrounds. In particular, the application of adaptive lighting adjustment and complex background separation effectively reduces the influence of environmental factors on recognition results, ensuring high-accuracy recognition under changing conditions.

[0038] 2. Enhanced system robustness: The multi-scale feature fusion module integrates feature representations at different scales, enhancing the system's adaptability to changes in object size within images. The deep learning model training module utilizes large-scale annotated datasets and transfer learning techniques to further enhance the model's generalization capabilities to new environments and scenarios, making the system more robust in the face of various practical application challenges.

[0039] 3. Achieve efficient real-time or near-real-time operation: The adaptive learning optimization module and the real-time performance assurance module work together to ensure that the system can quickly respond to and process image data in real-time or near-real-time conditions. This efficient operation is crucial for applications such as autonomous driving and medical imaging that require extremely high real-time performance. The technical solution of this invention meets the needs of these scenarios for fast and accurate recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Figure 2 is the adaptive multimodal image recognition system of the present invention.

[0041] Figure 2 This is a step diagram of the adaptive multimodal image recognition method of the present invention DETAILED DESCRIPTION

[0042] The purpose of this invention is to provide an adaptive multimodal image recognition system and method capable of highly accurate image recognition in a variety of lighting conditions and complex backgrounds. By integrating multiple innovative modules, this system implements adaptive image preprocessing, multimodal feature extraction, adaptive illumination adjustment, complex background separation, multiscale feature fusion, deep learning model training, adaptive learning optimization, multi-algorithm fusion decision-making, and performance evaluation and feedback, significantly improving the performance and application scope of image recognition.

[0043] The invention is described in detail as follows:

[0044] The adaptive preprocessing module optimizes images through a series of automated steps. First, the module analyzes the image using a light detection algorithm to determine the light level. If the light intensity is detected to be below a preset threshold (e.g., below 50 units), the module automatically initiates the denoising algorithm and sets the denoising intensity to a medium level (e.g., the denoising threshold is set to 15) to effectively remove image noise. Next, the contrast enhancement algorithm dynamically adjusts gamma correction parameters based on the light intensity. For example, in low light conditions (e.g., below 30 units), the gamma value is set to 1.2 to improve image contrast. Furthermore, the white balance adjustment algorithm automatically adjusts the gain values ​​of the RGB channels by calculating the image's color temperature and hue distribution. For example, when the color temperature is bluish (e.g., above 6500K), the gain of the red and green channels is increased, while the gain of the blue channel is reduced to achieve a natural color balance. Finally, after this series of adaptive adjustments, the module outputs an image with optimized brightness, contrast, and color, providing high-quality visual information for subsequent feature extraction and recognition. Through these precisely controlled parameters and automated processes, the adaptive preprocessing module significantly improves the efficiency and effectiveness of image processing.

[0045] The multimodal feature extraction module uses deep learning technology for efficient feature extraction: First, the input image passes through a CNN consisting of five convolutional layers, where the first two layers use 32 3x3 convolution kernels with a stride of 1, followed by two layers using 64 3x3 convolution kernels with a stride of 2, and the last layer uses 128 1x1 convolution kernels for feature compression. Each convolution layer is followed by a 2x2 maximum pooling layer with a stride of 2 to reduce the spatial dimension of the feature; then, the high-level features extracted by CNN are fed into a two-layer LSTM network, each LSTM layer contains 256 The multimodal feature extraction module uses a 1024-neuron fully connected layer to further process the features. The layer uses a time step of 0.05 seconds to process image sequences and capture temporal dynamic features. Next, the feature fusion layer integrates visual, infrared, and radar features using a weighted fusion strategy, with visual features weighted at 0.6, infrared features weighted at 0.25, and radar features weighted at 0.15, to enhance the contribution of each modality to the recognition task. Finally, the fused features are further processed through a fully connected layer containing 1024 neurons, and a ReLU activation function introduces nonlinearity to enhance the model's expressive power. Through these specific numerical parameters and a carefully designed process, the multimodal feature extraction module accurately captures and fuses key information from images, providing rich and robust feature support for object detection and classification tasks.

[0046] The adaptive illumination adjustment module optimizes the image illumination conditions through the following specific steps: First, the module analyzes the image through the illumination estimation algorithm and calculates the image brightness mean as 150 units and the standard deviation as 30 units to evaluate the illumination conditions; Then, based on the evaluation results, if the brightness mean is lower than the preset threshold (for example, lower than 180 units), the module automatically applies the global histogram equalization algorithm to improve the overall brightness; if the image has local overexposure or underexposure, the module uses the CLAHE algorithm, sets the contrast limit to 2.0, and the adjustment step size to 10 to enhance the local contrast; Then, the module simulates the image through the adaptive algorithm. Uniform lighting environment, such as adjusting the gamma value, setting the image gamma value within the range of 0.5 to 1.5, and dynamically adjusting it based on the mean brightness to reduce the impact of uneven lighting; In addition, the lighting consistency check unit ensures that the brightness variation of the adjusted image is controlled within ±10% to maintain lighting uniformity in different areas; Finally, the module outputs the image after lighting adjustment. For example, the adjusted mean brightness is increased to 175 units, and the standard deviation is controlled between 25 and 35 units, ensuring that the lighting uniformity of the image in different areas meets the preset standards, thereby providing more consistent and optimized visual data for subsequent feature extraction and recognition tasks. Through these precise numerical parameters and automated processes, the lighting adaptive adjustment module effectively improves the robustness and accuracy of image recognition.

[0047] The complex background segmentation module achieves precise object separation through the following detailed steps: First, the module uses background subtraction technology to calculate the pixel differences between consecutive frames, setting the frame difference threshold parameter to 25. When the brightness difference between pixels exceeds this threshold, it identifies them as foreground. Then, it performs morphological operations, using a 3x3 rectangular structuring element for erosion to remove noise, and then a 5x5 rectangular structuring element for dilation to fill small holes that may exist in the foreground objects. Next, it applies a deep learning foreground segmentation algorithm, such as Mask R-CNN, which uses the trained mask to accurately distinguish foreground and background with a confidence threshold of 0.7. The classification loss function is weighted to 0.5, and the bounding box regression loss function is weighted to 0.3 to ensure the accuracy of the segmentation results. In addition, the context information integration unit in the module uses the context of the scene to assist in the separation process. For example, it analyzes the relative position and size of objects and may set filtering conditions based on the average object size to exclude objects of unusual size. Finally, the module outputs a clearly separated target object image, providing a high-purity object image for subsequent processing steps, greatly improving the accuracy and robustness of recognition. Through these specific numerical parameters and steps, the complex background separation module effectively extracts the target object from the complex background.

[0048] The multi-scale feature fusion module achieves accurate recognition of targets of different sizes through the following steps: First, the module uses convolution kernels of different sizes in the CNN architecture, such as 3x3, 5x5 and 7x7, to extract features from details to semantics at different levels of the network; then, it constructs multi-scale features through the Feature Pyramid Network (FPN), sets side connections to combine the high-dimensional, low-resolution feature maps of the deep network with the low-dimensional, high-resolution feature maps of the shallow network, where upsampling uses bilinear interpolation and the ratio is set to 2 to gradually increase the resolution of the feature map; then, the feature fusion In the fusion strategy, a 1x1 convolution kernel is used for feature recalibration and channel adjustment, while a 3x3 convolution kernel is used for further spatial feature fusion, ensuring the effective combination of features at different scales. Furthermore, the downsampling operation in the module reduces the spatial size of the feature map through maximum pooling with a stride of 2 while retaining important information. Finally, the fused feature map is passed through the classification and detection networks for final target recognition and localization. The feature hierarchy in the feature pyramid may include four scales: 1 / 4, 1 / 8, 1 / 16, and 1 / 32 of the original size, to accommodate targets of different sizes. Through these specific numerical parameters and strategies, the multi-scale feature fusion module strengthens the system's comprehensive recognition capabilities for targets ranging from tiny to large.

[0049] The deep learning model training module achieves efficient training and domain adaptability through the following specific steps: First, the module uses a large-scale dataset containing more than 100,000 labeled samples, which cover a variety of categories and environmental conditions to ensure that the model has a wide range of generalization capabilities; then, it uses the cross-extraction loss function L ce To optimize the classification task, the loss function is defined as:

[0050]

[0051] Where N is the number of stars, y o，c is the one-hot encoding of the true label, y p，c is the probability distribution predicted by the model; then, using the transfer learning technology, the model parameters pre-trained on a large dataset (such as ImageNet) are used as a starting point, and fine-tuned on data in a specific field, through the transfer learning loss function L tl To further optimize the model, this function may incorporate new domain-specific features:

[0052] L tl =λL ce +(1-λ)L reg

[0053] Among them, L regis a regularization term used to control model complexity and prevent overfitting. λ is a proportional parameter that balances transfer learning and domain adaptability, and is usually set to 0.5. In addition, the module also uses data enhancement techniques such as random cropping, flipping, and color jittering to further improve the generalization ability of the model. Finally, through early stopping and model checkpointing strategies, training is stopped when no performance improvement is observed on the validation focus, and the best performing model is selected for saving and deployment. Through this process, deep The learning model training module ensures the rapid adaptation and continuous optimization of the model in new fields.

[0054] Adaptive Learning Optimization Module First, the module monitors the performance of the model in real-time and collects feedback on key indicators such as accuracy and recall. Data; Based on the feedback data, an adaptive learning rate adjustment algorithm is used, such as the learning rate decay formula η t+1 =η t α, where η t is the learning rate of the tth step, α is a decay factor less than 1 to adapt to the changes in model performance; the model weights are updated using the online speed-up descent method, and the formula is Here t represents the model weight at step t, J(w t ) is the loss function, is the gradient of the loss function with respect to the weight; the module controls the model complexity by introducing the regularization term Ω(w), such as L2 regularization, the formula is J reg (w t )=J(w t )+λ·Ω(w), where λ is the regularization coefficient to prevent overfitting; the module implements the model update strategy, such as using the exponential moving average to smooth the model weights, the formula is w EMA =β·w EMA +(1-β)·w t , where β is a smoothing coefficient, which usually takes a value between 0.9 and 0.999 to achieve stable update of model parameters.

[0055] The module generates a class prediction and a corresponding confidence score. A weighted voting mechanism is used to assign weights to each algorithm's predictions based on its performance on the validation set. For example, an algorithm with 90% accuracy on the validation set might receive a weight of 0.7, while one with 85% accuracy might receive a weight of 0.3. The module uses a Bayesian approach to probabilistically fuse the predictions of each algorithm, calculating the posterior probability distribution and updating the predictions using the formula P(y|x)∝P(x|y)P(y), where P(y|x) is the posterior probability of class y given observation x, P(x|y) is the likelihood probability, and P(y) is the prior probability of the class. Furthermore, the module considers a confidence threshold; predictions below a certain threshold (e.g., 0.5) are not included in the final decision, improving the system's robustness. Finally, the module outputs a final recognition result that integrates the strengths of multiple algorithms, ensuring high accuracy and robustness even in complex scenarios. Through this meticulous integration process, the multi-algorithm fusion decision module significantly improves the overall recognition performance of the system.

[0056] The performance evaluation and feedback module follows these steps to achieve continuous optimization of the system: First, the module sets performance monitoring thresholds, such as a processing speed target of 20 milliseconds per frame and an accuracy target of 99%. When performance indicators fall below these thresholds, they are automatically recorded and evaluation is triggered. Second, real-time data tracking tools are used to collect indicators such as processing speed, accuracy, and resource consumption. Visualization techniques such as line charts and bar charts are used to show how performance indicators change over time and the differences between different algorithms. Third, user satisfaction scores and specific suggestions are collected through online questionnaires and user feedback systems, with a user satisfaction target of 4 points or above ( A maximum score of 5 points); then, based on performance data and user feedback, the module develops an optimization plan, which may include adjusting the deep learning model's learning rate (for example, from 0.001 to 0.0005) or increasing data diversity; then, it conducts A / B testing, randomly assigning users to a control group and an experimental group, comparing performance before and after the optimization measures, such as the percentage increase in processing speed and the change in accuracy; finally, based on test results and user feedback, the module iteratively updates the algorithm, using automated deployment tools to tag the optimized algorithm with a version number and push it to the production environment, achieving steady performance improvements and continuous improvements in the user experience. Through this fine-tuned process, the performance evaluation and feedback module ensures the efficient operation of the system and the continuous satisfaction of user needs.

[0057] The real-time performance assurance module ensures the real-time performance of the algorithm through the following meticulous steps: First, the module sets up real-time monitoring to track system response time, with a target maximum latency of no more than 50 milliseconds to ensure that real-time processing standards are met. Next, it uses performance analysis tools to identify bottlenecks, such as locating specific function calls that cause CPU utilization to exceed 80%, and performs targeted optimizations on these bottlenecks, potentially reducing execution time by 50% through parallelization. Next, it performs code optimization, including algorithm simplification and memory access optimization, reducing the number of loop iterations, for example, from 100 to 50, thereby reducing memory latency. In addition, it applies model compression techniques, such as pruning 20% ​​of redundant neurons and quantizing model weights to 8-bit integers, reducing the model size to 75% of its original size while maintaining a minimum model accuracy of 95%. Then, it dynamically adjusts resource allocation based on system load, automatically adjusting thread priorities and memory allocation strategies when CPU utilization exceeds 85%. Finally, it implements adaptive load balancing, automatically adding processing threads or optimizing task queues when task wait times exceed a set threshold (for example, 25 milliseconds) to avoid overload. Through these specific parameter settings and optimization measures, the real-time performance assurance module ensures the system's rapid response and efficient processing capabilities under high-load environments.

[0058] The method of the adaptive multimodal image recognition system of the present invention is characterized by the following comprehensive and detailed steps:

[0059] Preliminary analysis of input image: The system first receives the input image and uses the image analysis algorithm to set the lighting condition threshold, such as 50 units, and the background complexity is divided into three levels: low, medium, and high, for preliminary analysis.

[0060] Adaptive preprocessing: Based on the analysis results, the adaptive preprocessing module automatically adjusts the denoising intensity to 80% of the preset threshold, enhances the contrast to 120% of the original contrast, and automatically adjusts the white balance through color temperature detection.

[0061] Multimodal feature extraction: The multimodal feature extraction module extracts the color, texture, and shape features of the image by setting color space conversion parameters, such as HSV to RGB conversion, and uses specific algorithms such as edge detection to extract depth information.

[0062] Adaptive lighting adjustment: The adaptive lighting adjustment module automatically applies histogram equalization or gamma correction according to lighting conditions, setting adjustment parameters such as gamma value from 0.5 to 1.5 to simulate a uniform lighting environment.

[0063] Complex background separation: The complex background separation module uses background subtraction technology, sets the frame difference threshold parameter to 25, and uses morphological operations such as 3x3 structure element dilation and erosion to separate the target object.

[0064] Multi-scale feature fusion: Build a feature pyramid, set the resolution ratios of different levels, such as 1 / 4, 1 / 8, 1 / 16, and 1 / 32 of the original size, and integrate features of different scales through fusion strategies.

[0065] Deep learning model training: A large-scale labeled dataset was used to train the deep learning model, with a batch size of 32 and a learning rate of 0.001. Transfer learning techniques were used for fine-tuning.

[0066] Adaptive learning optimization: Based on the training results, the adaptive learning optimization module dynamically adjusts the learning rate, such as to 0.0005, and applies regularization techniques to prevent overfitting.

[0067] Multi-algorithm fusion decision-making: The trained model is applied to the actual image recognition task. By setting a weighted voting mechanism, such as setting the visual feature weight to 0.6, the recognition results of different algorithms are integrated.

[0068] Performance evaluation and feedback: Collect performance data in actual applications, such as accuracy of no less than 99% and processing speed of less than 20 milliseconds per frame, and continuously evaluate system performance through user feedback.

[0069] System iterative optimization: Based on performance evaluation results and user feedback, iteratively optimize system parameters, such as adjusting model structure or updating data enhancement strategies.

[0070] Real-time performance guarantee: Ensure the system operates efficiently in real-time or near real-time conditions, set the maximum latency threshold to 50 milliseconds, and perform resource scheduling and performance optimization when necessary.

[0071] Through these specific, clear and comprehensive steps, the adaptive multimodal image recognition system of the present invention can adapt to changing application requirements and environmental conditions and achieve efficient and accurate image recognition.

[0072] The system and method of the present invention not only improve the accuracy of image recognition through the collaborative work of the above modules, but also enhance the adaptability and generalization ability of the system, enabling it to operate stably in a variety of practical application scenarios.

Claims

1. An adaptive multimodal image recognition system, characterized in that: include: Adaptive preprocessing module, used to automatically adjust preprocessing parameters such as denoising, contrast enhancement, and white balance adjustment according to the lighting conditions of the image; Multimodal feature extraction module, which is used to extract color, texture, shape, and depth information from images, and uses convolutional neural networks and recurrent neural networks to extract spatial and temporal series features; Illumination adaptive adjustment module, used to perform illumination correction to reduce the impact of illumination changes on recognition performance; The complex background separation module is used to effectively separate the foreground object and background using background subtraction technology and foreground segmentation technology; the multi-scale feature fusion module is used to construct feature representations of different scales and integrate these features through feature fusion algorithms to enhance recognition capabilities; A deep learning model training module is used to train deep learning models using large-scale labeled datasets and adopt transfer learning techniques to improve the generalization ability of the model in new domains; Adaptive learning optimization module, used to dynamically adjust model parameters according to the accuracy of recognition results to achieve online learning and updating; The multi-algorithm fusion decision module is used to integrate the recognition results of different algorithms through a weighted voting mechanism or Bayesian method to improve the overall recognition performance; the performance evaluation and feedback module is used to continuously evaluate system performance, collect user feedback, and continuously optimize the algorithm based on the evaluation results and user feedback.

2. The system according to claim 1, wherein: The adaptive pre-processing module further includes an automatic denoising algorithm for reducing image noise.

3. The system according to claim 1, wherein: The multimodal feature extraction module further includes deep learning technology for extracting high-level features of the image.

4. The system according to claim 1, wherein: The adaptive illumination adjustment module further includes an illumination estimation algorithm for estimating the illumination conditions of the image.

5. The system according to claim 1, wherein: The complex background separation module further includes a foreground segmentation technology for separating the moving object from the background.

6. From claims 1 to 5, the system further comprises other modules or algorithms for improving recognition accuracy.

7. The system according to any one of claims 1 to 6, characterized in that The system can be applied to fields such as autonomous driving, medical imaging, and security monitoring.

8. The system according to any one of claims 1 to 7, characterized in that The system also includes a real-time performance guarantee module for efficient operation under real-time or near real-time conditions.

9. The system according to any one of claims 1 to 8, characterized in that The system also includes a performance evaluation and feedback module for improving user satisfaction.

10. A method of using the adaptive multimodal image recognition system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Receives an input image and performs preliminary analysis on it to determine lighting conditions and background complexity; Use the adaptive pre-processing module to perform image denoising, contrast enhancement and white balance adjustment; Extract the color, texture, shape and depth information of the image through the multimodal feature extraction module; Use the adaptive illumination adjustment module to correct the image illumination to adapt to different lighting environments; Use the complex background separation module to perform background subtraction and foreground segmentation on the image to separate the target object; Construct multi-scale feature representation and integrate features of different scales through a multi-scale feature fusion module; Use the deep learning model training module to learn and perform pattern recognition training on the extracted features; Based on the training results, the model parameters are dynamically adjusted through the adaptive learning optimization module to improve recognition accuracy; Apply the trained model to actual image recognition tasks and integrate the recognition results of different algorithms through the multi-algorithm fusion decision module; Collect performance data in actual applications and continuously evaluate system performance through performance evaluation and feedback modules; Iteratively optimize the system based on performance evaluation results and user feedback to adapt to changing application requirements and environmental conditions; Ensure the efficient operation of the system under real-time or near real-time conditions, and perform resource scheduling and performance optimization when necessary.

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