Automatic labeling method based on deep learning intelligent algorithm

Abstract

The invention discloses an automatic labeling method based on a deep learning intelligent algorithm, and belongs to the field of image processing and computer vision. According to the method, automatic annotation of large-scale image data is realized by means of a deep learning technology, and the annotation efficiency and accuracy are improved. The method comprises the following steps of: firstly, removing noise through image preprocessing, standardizing the size and ensuring the input quality; identifying and classifying an image target by using models such as a convolutional neural network, and automatically generating annotation information; and finally, a result is adjusted through a self-adaptive optimization mechanism, so that the labeling precision under different categories and scenes is ensured. According to the method, large-scale data sets can be efficiently processed, manual participation is reduced, labeling consistency is improved, multi-level labeling requirements are supported, the problems that traditional manual labeling is low in efficiency and large in error are solved, the method is widely applied to the fields of automatic driving, medical images and the like, the labor cost is reduced, and labeling accuracy and applicability are improved.

Description

Technical Field

[0001] This invention belongs to the field of computer vision, specifically relating to an automated annotation method based on deep learning intelligent algorithms. It is particularly suitable for automatic recognition and annotation of image content in image processing scenarios through deep learning models. Background Technology

[0002] With the development of artificial intelligence and computer vision technologies, image annotation has been widely applied in various fields. Traditional manual annotation methods typically require significant time and human resources, making them inefficient for processing massive amounts of image data and failing to meet automation needs. Meanwhile, although deep learning technology has made significant progress in image recognition and classification, existing automated annotation technologies still have some problems:

[0003] Traditional image annotation relies on manual operation. As the scale of data increases, the workload of manual annotation becomes enormous and is easily affected by human error. Annotating large-scale datasets is not only time-consuming but also requires a significant investment of funds and manpower, especially in fine-grained annotation tasks, where the annotation complexity further increases.

[0004] While deep learning models excel in tasks such as object detection and image segmentation, the generalization ability of existing methods is limited. Model performance depends heavily on the diversity and quality of training data, and in some cases, it struggles to handle new scenes or unseen targets. Furthermore, model training requires a large amount of labeled data, necessitating manual intervention in the initial stages.

[0005] Although deep learning models can automatically generate image annotations, existing methods often perform poorly in boundary handling, instance differentiation, and key point detection in some complex scenarios, failing to meet the high requirements for annotation accuracy in certain fields, especially applications such as medical image analysis and autonomous driving that heavily rely on accurate annotation.

[0006] In summary, existing technologies have significant shortcomings in terms of annotation cost, generalization ability, and annotation accuracy. There is an urgent need for a more efficient and accurate automated annotation method to improve the efficiency and quality of large-scale image processing tasks. Summary of the Invention

[0007] To address the problems of low efficiency, high cost, and insufficient annotation accuracy in existing image annotation methods, this invention provides an automated annotation method based on deep learning intelligent algorithms. This method can efficiently and accurately annotate image content in complex scenes, adapt to various image processing tasks, and is especially suitable for fields requiring large-scale image annotation.

[0008] This invention is implemented as follows:

[0009] An automated annotation method based on deep learning intelligent algorithms, the method comprising the following steps:

[0010] Step 1, Image Data Acquisition and Preprocessing: Acquire the original images to ensure sample diversity and representativeness. Standardize the input image data, including image scaling, noise removal, color correction, contrast enhancement, edge detection, data format conversion, and labeling information, to ensure the consistency and quality of the input image data and reduce interference caused by factors such as lighting and noise in the images.

[0011] Step 2, Building and Training a Deep Learning Model: Based on the preprocessed image data, a Convolutional Neural Network (CNN) or a Deep Convolutional Generative Adversarial Network (DCGAN) deep learning model is used for supervised learning through a large-scale labeled dataset to learn the feature patterns in the image, including edges, shapes, textures, and colors, and to generate a preliminary labeled model. This model extracts features through multiple convolutional layers, reduces dimensionality using pooling layers, and performs classification or regression through fully connected layers.

[0012] Step 3, Automatic annotation generation: The trained deep learning model is used to automatically annotate the new input image data, identify different target objects in the image, and generate annotation information according to predefined categories. The annotation information includes the category, location (bounding box or pixel-level segmentation), and confidence score of the target object. The annotation results are exported in JSON or XML format, which is suitable for different application scenarios.

[0013] Step 4, Adaptive Optimization and Iterative Learning: The adaptive optimization mechanism adjusts model parameters in real time, including learning rate, kernel size, and number of layers. Adjustments are based on feedback from the image dataset, including annotation errors and model performance. The system automatically adjusts model weights and biases to reduce overfitting. This improves the model's annotation accuracy in different image scenarios. The optimization mechanism uses gradient descent or the Adam optimizer algorithm, supporting fine-tuning on different datasets to adapt to diverse annotation needs. Iterative learning, after new image annotations are completed, compares the automatically generated annotation results with standard data in the existing annotation database, calculates the error value, and automatically adjusts the model's weight parameters for retraining and optimization.

[0014] Step 5, Model Update and Optimization: After each round of iteration and optimization, the parameters of the deep learning model are updated based on feedback to generate more accurate annotation results. The optimized model is applied to the subsequent automatic image annotation process to continuously improve the efficiency and accuracy of annotation.

[0015] Furthermore, in step 1, the image preprocessing can handle image data of different resolutions and formats, including JPEG, PNG, TIFF, and BMP, and supports batch processing to meet the annotation needs of large-scale image datasets.

[0016] Furthermore, in step 1, the image preprocessing involves standardizing the image using the following formula:

[0017]

[0018] Among them, I norm These are standardized image data, where I is the original image and μ is the... I and σ I These represent the mean and standard deviation of the image, respectively.

[0019] Furthermore, in step 2, the calculation formula for the features extracted by the convolutional layer is as follows:

[0020]

[0021] Among them, F i,j I represents the output value of the feature map. m,n K represents the pixel values ​​of the input image. i-m,j-n These are the weight parameters of the convolution kernel.

[0022] Furthermore, in step 2, the deep learning model is optimized through transfer learning. A pre-trained deep learning model is used, and the weights pre-trained on the ImageNet large-scale dataset are fine-tuned on the target dataset to improve the model's adaptability and annotation accuracy in specific scenarios. During the transfer learning process, some convolutional layers are frozen, and only fully connected layers or some high-order feature extraction layers are trained, thereby reducing computational costs, accelerating training speed, and retaining the generality for low-order features.

[0023] Furthermore, in step 3, the automatically generated annotation supports multi-level annotation, which can perform multi-level annotation on multiple targets and multiple categories of objects in complex image scenes. Specifically, it covers instance-level annotation, semantic-level annotation, pixel-level segmentation and bounding box-level annotation of multiple target objects in the same image, to meet the needs of different application scenarios.

[0024] Furthermore, in step 3, the automatically generated annotation output supports multiple annotation formats, including bounding boxes, which generate the bounding box coordinates and category labels of the target object to locate the target position; semantic segmentation, which assigns a category label to each pixel, suitable for high-precision annotation scenarios; instance segmentation, which distinguishes different instances of the same category based on pixel-level segmentation, providing more refined results; and key point detection, which annotates the key point positions of the object for precise positioning in specific scenarios.

[0025] Furthermore, in step 3, the annotation results are presented to the user through a visual interface, allowing the user to manually verify or correct them. The automatic annotation generation process is optimized using the joint loss function of classification and bounding box regression tasks, with the optimization formula as follows:

[0026] L = L cls (p,p * )+λL reg (t,t * (3)

[0027] Among them, L cls L represents the classification loss. reg Let p represent the bounding box regression loss, where p is the predicted class probability. * The true class is t, and the predicted bounding box parameters are t. * λ represents the true bounding box parameters, and λ is the balance coefficient between classification loss and regression loss.

[0028] Furthermore, in step 4, the adaptive optimization mechanism includes a feedback-based dynamic learning rate adjustment mechanism, which can automatically adjust the model's learning rate according to the changes in the accuracy of the annotation results and the loss function, ensuring that the learning rate is gradually reduced when the annotation accuracy improves to prevent overfitting; and automatically increasing the learning rate when the annotation results have large errors, so that the model can adjust the weights more quickly and improve the annotation effect.

[0029] Furthermore, in step 4, the calculation error adopts the mean square error (MSE) formula:

[0030]

[0031] Among them, y i Represents the actual labeled value. This represents the predicted value, where n is the number of samples.

[0032] Furthermore, the automated annotation method can be integrated into cloud services or local servers, supporting image annotation services through API interfaces, making it convenient to apply in actual production environments.

[0033] The beneficial effects of this invention are:

[0034] 1. It achieves high efficiency and automation in image annotation, reducing manual intervention and lowering annotation costs;

[0035] 2. Provides support for multiple annotation types to meet the annotation needs of different image processing tasks;

[0036] 3. Through the adaptive optimization mechanism, the accuracy of annotation can be effectively improved, ensuring the model's performance in complex scenarios;

[0037] 4. Introduce automatic iterative learning methods to enable the model to be continuously optimized and improved during the annotation process, thereby enhancing its generalization ability;

[0038] 5. The method of this invention has high scalability and flexibility, and is applicable to large-scale image data annotation tasks in multiple fields. The invention will be further described below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0039] Figure 1 This is a flowchart of the automated annotation method based on deep learning intelligent algorithms of the present invention. Detailed Implementation

[0040] like Figure 1 As shown, this embodiment provides an automated annotation method and system based on deep learning intelligent algorithms. The automated annotation method based on deep learning intelligent algorithms includes the following steps:

[0041] Step 1, Image Data Acquisition and Preprocessing:

[0042] 1. Image data acquisition:

[0043] a) Acquire raw images from multiple sources, including industrial cameras, smartphones, and drones, to ensure sample diversity and representativeness.

[0044] b) Store the collected data, perform quality checks (remove blurry images), label the data (record shooting time, location, etc.), and perform multi-source fusion to improve sample coverage.

[0045] 2. Image preprocessing:

[0046] The acquired image data includes complex backgrounds and different object shapes. After data acquisition, the images are preprocessed through standardization and enhancement operations, including image scaling, noise removal, color correction, contrast adjustment, edge detection, data format conversion, and label information processing.

[0047] It can handle image data of different resolutions and formats, including common formats such as JPEG, PNG, TIFF, and BMP, and supports batch processing to meet the annotation needs of large-scale image datasets.

[0048] To ensure the consistency and quality of input data, reduce interference from factors such as lighting and noise, and ensure that the image quality meets the standards and conforms to the input format of deep learning models, so as to provide reliable basic data for model training and improve the generalization ability of models under different conditions.

[0049] The preprocessed image is standardized using the following formula:

[0050]

[0051] Among them, I norm These are standardized image data, where I is the original image and μ is the... I and σ I These represent the mean and standard deviation of the image, respectively.

[0052] Step 2, Building and Training the Deep Learning Model:

[0053] 1. Model selection and construction:

[0054] Based on preprocessed image data, a Convolutional Neural Network (CNN), a Deep Convolutional Generative Adversarial Network (DCGAN), or other deep learning models are used for supervised learning on a large-scale labeled dataset to learn feature patterns in the images, including edges, shapes, textures, and colors, and generate a preliminary labeled model. This model extracts features through multiple convolutional layers, reduces dimensionality using pooling layers, and performs classification or regression through fully connected layers.

[0055] 2. Feature Extraction and Optimization

[0056] a) Convolutional layer processing:

[0057] Feature extraction is performed on the preprocessed image. In this stage, a convolutional neural network layer of a deep learning model is used to process the image and extract key features, including edge information, texture features, and color distribution. Through a series of convolution, pooling, and activation operations, a set of high-dimensional feature vectors is generated, representing the key features of the image.

[0058] The feature extraction process uses convolution operations, and the calculation formula is as follows:

[0059]

[0060] Among them, F i,j I represents the output value of the feature map. m,n K represents the pixel values ​​of the input image. i-m,j-n These are the weight parameters of the convolution kernel.

[0061] b) Deep feature extraction:

[0062] For complex image scenes, features extracted by shallow convolution alone are insufficient to meet the requirements. Deep features can capture more abstract and essential patterns. Multi-layer convolution and pooling structures are used to extract deep features, enhancing the model's ability to express complex features.

[0063] 3. Transfer learning optimization:

[0064] Deep learning models can be optimized through transfer learning, which involves using pre-trained deep learning models and fine-tuning them on specific target datasets based on weights pre-trained on large-scale datasets such as ImageNet.

[0065] To improve the model's adaptability and annotation accuracy in specific scenarios.

[0066] During transfer learning, some convolutional layers can be frozen, and only fully connected layers or some high-order feature extraction layers can be trained, thereby reducing computational costs, speeding up training, and preserving the generalizability to low-order features.

[0067] Step 3, automatically generate annotations:

[0068] 1. Labeling Generation: The extracted feature vectors are input into a pre-trained deep learning model. This model learns from the data in the labeling database and can automatically learn the features of objects in the image. After the feature vectors are input into the model, labeling information is generated according to the predefined categories. The labeling information includes object category, location (bounding box or pixel-level segmentation), confidence score, etc.

[0069] 2. Multi-level annotation support: Supports multi-level annotation of multiple targets and categories of objects in complex image scenes, including instance-level annotation, semantic-level annotation, pixel-level segmentation, and bounding box-level annotation of multiple target objects in the same image, meeting the needs of different application scenarios. For example, in autonomous driving applications, it can not only annotate the categories of vehicles and pedestrians, but also annotate the boundaries and contours of each object, providing accurate positioning information.

[0070] 3. Result Output: The annotation output supports multiple annotation formats, including:

[0071] Bounding Box: For a target object in an image, generate the bounding box coordinates and category label of its rectangle to locate the target position;

[0072] Semantic segmentation: For each pixel in an image, a category label is assigned to achieve pixel-level annotation, which is suitable for scenarios that require high-precision annotation;

[0073] Instance segmentation: Building upon pixel-level segmentation, it further distinguishes different instances of the same category in an image, providing more refined annotation results;

[0074] Keypoint detection: Annotating the location of key points of objects in an image (such as joints of the human body or feature points of objects) for precise localization in specific application scenarios;

[0075] The annotation results can be exported as JSON or XML format, suitable for different application scenarios.

[0076] 4. Loss Function Optimization: The initially generated annotation results are presented to the user through a visual interface, allowing for manual verification or correction. The automatic annotation generation process is optimized using the joint loss function of the classification and bounding box regression tasks. The optimization formula is as follows:

[0077] L = L cls (p,p * )+λL reg (t,t * (3)

[0078] Among them, L cls L represents the classification loss. reg Let p represent the bounding box regression loss, where p is the predicted class probability. * The true class is t, and the predicted bounding box parameters are t. * λ represents the true bounding box parameters, and λ is the balance coefficient between classification loss and regression loss.

[0079] Step 4, Adaptive Optimization and Iterative Learning:

[0080] 1. Adaptive Optimization: Through an adaptive optimization mechanism, model parameters, including learning rate, kernel size, and number of network layers, are adjusted in real time. Adjustments are based on feedback from image datasets, such as annotation errors and model performance. This information is used to automatically optimize model weights and biases, reducing overfitting and improving annotation accuracy in different image scenarios. In terms of implementation, this optimization mechanism supports common algorithms such as gradient descent and the Adam optimizer, and can be fine-tuned for different datasets to adapt to diverse annotation needs.

[0081] The adaptive optimization mechanism includes a feedback-based dynamic learning rate adjustment mechanism, which can automatically adjust the model's learning rate according to the changes in the accuracy of the annotation results and the loss function. This ensures that the learning rate is gradually reduced as the annotation accuracy improves, thereby preventing overfitting. When the annotation results show large errors, the learning rate is automatically increased so that the model can adjust the weights more quickly and improve the annotation effect.

[0082] 2. Iterative Learning: To further improve the accuracy of annotation, an iterative optimization mechanism is introduced. After each new image is annotated, the automatically generated annotation results are compared with standard data in the existing annotation database to calculate the error value. If the error exceeds a preset threshold (this threshold needs to be set according to the specific scenario; for example, the IoU in object detection can be set to 0.5-0.9, the accuracy of image classification tasks can be set to 90%-99.9%, and the pixel error of key point annotation can be set to 1-5 pixels, etc. The threshold for high-precision scenarios such as medical image annotation will be more stringent), the system will automatically adjust the model's weight parameters, retrain and optimize until the error converges to an acceptable range.

[0083] Error calculation uses the mean square error (MSE) formula:

[0084]

[0085] Among them, y i Represents the actual labeled value. This represents the predicted value, where n is the number of samples.

[0086] Step 5, Model Update and Optimization:

[0087] After each iteration of optimization, the system updates the parameters of the deep learning model based on feedback, generating more accurate annotation results. The optimized model will then be applied to subsequent automatic image annotation processes, continuously improving the efficiency and accuracy of annotation.

[0088] This invention achieves fully automated and efficient image annotation by combining deep learning technology and automated image processing workflows. Compared with traditional manual annotation methods, this invention not only reduces human intervention but also significantly improves the efficiency and accuracy of image annotation. It is also highly scalable, supporting multiple image formats such as JPEG, PNG, TIFF, and BMP. It can be integrated into cloud services or local servers, providing annotation services through API interfaces. It is widely applicable to large-scale image data processing and computer vision applications, including:

[0089] The detection and labeling of target objects such as pedestrians, vehicles, and traffic signs in autonomous driving enhances the perception capabilities of driver assistance systems.

[0090] Automatic identification and annotation of lesion areas in medical images, such as tumors and organ outlines in CT and MRI images, to support clinical diagnosis;

[0091] Behavior recognition and target tracking in intelligent monitoring systems enhance the intelligence level of monitoring systems by automatically labeling people, vehicles, etc. in images or videos.

[0092] Image retrieval and classification improves the accuracy and efficiency of image retrieval systems by automatically labeling large-scale image datasets.

[0093] An automated annotation method based on deep learning intelligent algorithms automates the entire process from image data acquisition to annotation generation, including key steps such as data preprocessing, feature extraction, annotation generation, and iterative optimization. This method leverages the powerful feature extraction capabilities of deep learning models, combined with a feedback mechanism to continuously improve annotation accuracy, thereby significantly enhancing annotation quality while maintaining efficiency.

[0094] An automated annotation system based on deep learning intelligent algorithms, the system comprising:

[0095] The image data acquisition module is used to acquire raw image data, which can come from various sources such as industrial cameras, smartphones, or drones.

[0096] The data preprocessing module preprocesses the acquired image data, including operations such as noise reduction, color correction, and image scaling, to ensure that the image quality meets the input requirements of the deep learning model.

[0097] The feature extraction module uses the convolutional layers of a deep learning model to extract features from the preprocessed image and generate the image's feature vector.

[0098] The annotation generation module analyzes and processes feature vectors using a pre-trained deep learning model to automatically generate preliminary annotations for the image. These annotations can include object categories, bounding boxes, key points, etc.

[0099] The iterative optimization module compares the initial annotation results with the existing annotation database, continuously optimizes the accuracy of the annotation through a feedback mechanism, and uses the optimized results to update the deep learning model.

[0100] Identify the image samples to be labeled and the labeling requirements, and develop a detailed implementation plan.

[0101] 1. Data Acquisition Section

[0102] ① Image acquisition: Raw image data is acquired through image acquisition modules (such as industrial cameras, smartphones, or drones) to ensure the diversity and representativeness of image samples.

[0103] ② Data storage: The acquired image data is stored in a database to ensure data integrity and traceability for subsequent processing.

[0104] ③ Quality inspection: Perform quality checks on the acquired images, remove blurry or non-standard images, and ensure the quality of data for subsequent processing.

[0105] ④ Data labeling: Initial labeling of images, recording the shooting time, location and related information of each image to provide background information for subsequent analysis.

[0106] ⑤ Multi-source data fusion: Image data obtained from different sources are fused to improve sample diversity and ensure coverage of different scenes and object categories.

[0107] 2. Data preprocessing section:

[0108] ① Image denoising: Applying image processing algorithms to denoise the original image, improving image quality, and eliminating the impact of noise on feature extraction.

[0109] ② Color correction: Color correction is performed to address color differences between different acquisition devices to ensure image color consistency.

[0110] ③ Image scaling: Standardize the image to meet the input requirements of the deep learning model and ensure the uniformity of the input data.

[0111] ④ Data augmentation: Image data is augmented through rotation, flipping, cropping, etc., to generate more variations and improve the robustness of the model.

[0112] ⑤ Data format conversion: Convert the image into a format suitable for deep learning model processing (such as JPEG, PNG) to ensure input compatibility.

[0113] ⑥ Label information processing: The label information corresponding to the image is formatted for use in subsequent model training.

[0114] 3. Feature extraction section:

[0115] ① Convolutional layer processing: Convolutional neural networks are used to extract features from the preprocessed image and generate high-dimensional feature vectors to represent the key information of the image.

[0116] ② Feature selection: By analyzing the extracted features, select the features that have a significant impact on the annotation results, reduce redundant information, and improve the performance of the model.

[0117] ③ Feature normalization: The extracted features are normalized so that they can be compared on the same scale, thereby improving the training efficiency of the model.

[0118] ④ Feature combination: Combine multiple features into a new feature vector to increase the model's expressive power and improve the accuracy of annotation.

[0119] ⑤ Feature visualization: Visualize the extracted features to help analyze the model's learning performance and feature discrimination ability.

[0120] ⑥ Deep feature extraction: Multi-layer convolution and pooling structures are used to extract deep features, enhancing the model's ability to express complex features.

[0121] 4. Annotation generation section:

[0122] ① Train the model: Use a labeled database to train the deep learning model so that it can recognize different object categories and locations.

[0123] ② Label generation: Input the extracted feature vector and generate preliminary labeling results through the trained model, including information such as object category and bounding box.

[0124] ③ Visual presentation: The generated annotation results are presented to users in a visual form, which facilitates manual verification and correction.

[0125] ④ Preliminary verification: Select sample images to verify the preliminary annotation results to ensure that the generated annotations conform to the actual situation.

[0126] ⑤ Labeling correction: Users can manually correct the initial labeling results to further improve the accuracy of the labeling.

[0127] ⑥ Result fusion: The annotation results generated multiple times are merged, and the credibility of the final annotation is improved by methods such as weighted averaging.

[0128] 5. Iterative optimization section:

[0129] ① Result comparison: The automatically generated annotation results are compared with the existing annotation database to calculate the error and identify the areas that need optimization.

[0130] ② Model feedback: By analyzing the sources of error, the parameters of the deep learning model are adjusted and retrained to improve the model's accuracy.

[0131] ③ Iterate continuously, comparing the generated annotations with the results, until the annotation results reach the predetermined accuracy.

[0132] ④ Error analysis: Conduct in-depth analysis of the causes of annotation errors, identify systemic problems, and make targeted corrections.

[0133] ⑤ Model validation: Regularly validate the effectiveness of the model to ensure its applicability and accuracy on new datasets.

[0134] ⑥ Feedback mechanism: Establish a user feedback channel to collect user opinions on the annotation results and further optimize the model.

[0135] This invention utilizes deep learning technology to automate image annotation, improving both efficiency and accuracy. Compared to traditional manual annotation methods, this invention significantly reduces time costs, enhances large-scale image processing capabilities, and is applicable to various scenarios requiring image annotation, providing crucial support for the automation development of the image processing field.

[0136] Finally, it should be noted that the above is only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention (such as the application of various formulas, the order of steps, etc.) without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An automated annotation method based on deep learning intelligent algorithms, characterized in that, The method includes the following steps: Step 1, Image Data Acquisition and Preprocessing: Acquire the original image to ensure sample diversity and representativeness. Standardize the input image data, including image scaling, noise removal, color correction, contrast enhancement, edge detection, data format conversion, and label information, to ensure the consistency and quality of the input image data and reduce interference caused by factors such as lighting and noise in the image. Step 2, Building and Training a Deep Learning Model: Based on the preprocessed image data, a Convolutional Neural Network (CNN) or a Deep Convolutional Generative Adversarial Network (DCGAN) deep learning model is used for supervised learning through a large-scale labeled dataset to learn the feature patterns in the image, including edges, shapes, textures, and colors, and to generate a preliminary labeled model. This model extracts features through multiple convolutional layers, reduces dimensionality using pooling layers, and performs classification or regression through fully connected layers. Step 3, Automatic annotation generation: The trained deep learning model is used to automatically annotate the new input image data, identify different target objects in the image, and generate annotation information according to predefined categories. The annotation information includes the category, location (bounding box or pixel-level segmentation) and confidence score of the target object. The annotation results are exported in JSON or XML format, which is suitable for different application scenarios. Step 4, Adaptive Optimization and Iterative Learning: The adaptive optimization mechanism adjusts model parameters in real time, including learning rate, kernel size, and number of layers. Adjustments are based on feedback from the image dataset, including annotation error and model performance. The model's weights and biases are automatically adjusted to reduce overfitting. This improves the model's annotation accuracy in different image scenes. The optimization mechanism uses gradient descent or the Adam optimizer algorithm, supporting fine-tuning on different datasets to adapt to diverse annotation needs. Iterative learning, after new image annotations are completed, compares the automatically generated annotation results with standard data in the existing annotation database, calculates the error value, and automatically adjusts the model's weight parameters for retraining and optimization. Step 5, Model Update and Optimization: After each round of iteration and optimization, the parameters of the deep learning model are updated based on feedback to generate more accurate annotation results. The optimized model is applied to the subsequent automatic image annotation process to continuously improve the efficiency and accuracy of annotation.

2. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 1, the image preprocessing can handle image data of different resolutions and formats, including JPEG, PNG, TIFF, and BMP, and supports batch processing to meet the annotation needs of large-scale image datasets.

3. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 1, the image preprocessing involves standardizing the image using the following formula: Among them, I norm These are standardized image data, where I is the original image and μ is the... I and σ I These represent the mean and standard deviation of the image, respectively.

4. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 2, the formula for calculating the features extracted by the convolutional layer is: Among them, F i,j I represents the output value of the feature map. m,n K represents the pixel values ​​of the input image. i-m,j-n These are the weight parameters of the convolution kernel.

5. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 2, the deep learning model is optimized through transfer learning. A pre-trained deep learning model is used, and the weights pre-trained on the ImageNet large-scale dataset are fine-tuned on the target dataset to improve the model's adaptability and annotation accuracy in specific scenarios. During transfer learning, some convolutional layers are frozen, and only fully connected layers or some high-order feature extraction layers are trained, thereby reducing computational costs, speeding up training, and preserving the generalizability to low-order features.

6. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 3, the automatically generated annotation supports multi-level annotation, which can perform multi-level annotation on multiple targets and multiple categories of objects in complex image scenes. Specifically, it covers instance-level annotation, semantic-level annotation, pixel-level segmentation and bounding box-level annotation of multiple target objects in the same image, to meet the needs of different application scenarios.

7. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 3, the automatically generated annotation output supports multiple annotation formats, including bounding boxes, which generate the bounding box coordinates and category labels of the target object to locate the target position; and semantic segmentation, which assigns a category label to each pixel, suitable for high-precision annotation scenarios. Example Segmentation, based on pixel-level segmentation, distinguishes different instances of the same category, providing more refined results; Key point detection marks the location of key points on an object for precise localization in specific scenarios.

8. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 3, the annotation results are presented to the user through a visual interface, allowing the user to manually verify or correct them. The automatic annotation generation process is optimized using the joint loss function of classification and bounding box regression tasks, with the optimization formula as follows: L=L cls (p,p * )+λL reg (t,t * ) (3) Among them, L cls L represents the classification loss. reg Let p represent the bounding box regression loss, where p is the predicted class probability. * The true class is t, and the predicted bounding box parameters are t. * λ represents the true bounding box parameters, and λ is the balance coefficient between classification loss and regression loss.

9. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 4, the adaptive optimization mechanism includes a feedback-based dynamic learning rate adjustment mechanism, which can automatically adjust the model's learning rate according to the changes in the accuracy of the annotation results and the loss function, ensuring that the learning rate is gradually reduced as the annotation accuracy improves, thus preventing overfitting. When the annotation results show a large error, the learning rate is automatically increased, enabling the model to adjust the weights more quickly and improve the annotation effect.

10. The automated annotation method based on deep learning intelligent algorithms according to claim 1, characterized in that, In step 4, the calculation error is performed using the mean square error (MSE) formula: Among them, y i Represents the actual labeled value. This represents the predicted value, where n is the number of samples.

Images