266 results about "Image labeling" patented technology

The invention discloses a pavement crack semantic segmentation method based on Transform and CNN architecture, and relates to the technical field of pavement crack detection, and the method comprises the steps: obtaining a pavement image, marking a crack disease image in the pavement image through an image marking tool, and constructing a data set based on the marked crack disease image; preprocessing the data set, and constructing and training a self-adaptive semantic segmentation model combining a self-attention neural network and a convolutional neural network based on the preprocessed data set; and deploying the trained adaptive semantic segmentation model to a local platform, and inputting the crack disease image into the adaptive semantic segmentation model for segmentation and marking. According to the method, a dynamic scale selector composed of a texture perceptron and a scale regulation and control unit is introduced, the size of a convolution kernel is dynamically adjusted according to the local texture complexity of an image, and fine modeling of different structure areas is achieved.

The invention discloses a traffic image labeling method and device based on a D-S evidence theory and a medium, and relates to the technical field of image labeling. According to the method, two different types of real-time models are simultaneously utilized to process the same image in parallel, and two uncertainty detection results of the same image are obtained; a KM algorithm is utilized to match targets in two uncertainty detection results, then a prior credibility score is utilized to reduce a conflict coefficient, and two different types of small model prediction results are combined through an uncertainty theory. According to the traffic image labeling method and device based on the D-S evidence theory and the medium, the strong generalization ability of the open set model is utilized to make up for the defects of a closed set model, the use of a large model to improve the hardware dependence is avoided, the precision problem of a single small model is made up under the condition that the efficiency is ensured, and the iteration duration of the model is shortened.

The present application relates to the field of automatic data labeling, and particularly relates to an image labeling method, a computer device for implementing the method, and a computer storage medium for implementing the method. The image labeling method comprises: using an image segmentation model to perform segmentation processing on original images from multiple viewpoints, so as to output segmented mask images of the original images from the viewpoints, wherein each segmented mask image includes labeling information for indicating the contour of a target object; using a neural radiance field to perform multi-viewpoint fusion on the segmented mask images from the viewpoints, so as to correct single-viewpoint errors, and generating corrected first mask images; and performing multi-frame timing fusion on the first mask images on the basis of a timing relationship, so as to correct single-frame errors, and generating a second mask image from the viewpoint of a bird's eye view.

The invention provides a three-dimensional target marking method and device based on a point cloud data-assisted two-dimensional image, and the method comprises the steps: fusing precise three-dimensional point cloud information obtained by a laser radar and two-dimensional image data collected by a camera based on a point cloud data and image data cooperation three-dimensional marking method; and a dynamic high-precision three-dimensional target marking system is constructed. Specifically, a space-image relationship is established through three-dimensional point cloud information and a two-dimensional image plane, and three-dimensional information of a target is mapped into a two-dimensional image, so that geometric and semantic information labeling is carried out on the target in the two-dimensional image, information loss of two-dimensional visual data in dimensions such as depth, attitude and space structure is made up, and the accuracy of the three-dimensional information labeling is improved. And meanwhile, when the three-dimensional point cloud is unmeasurable, a three-dimensional target labeling mode is dynamically adjusted in combination with two-dimensional image labeling information. According to the method, through dynamic high-precision three-dimensional target labeling, the obtained image-label can be used for training and reasoning of a three-dimensional target detection network, and the method has a wide application prospect.

The invention discloses an intelligent bone marrow megakaryocyte morphology detection method which comprises the following steps: S1, data preparation: collecting and preprocessing a digital large map of a bone marrow smear, labeling megakaryocytes, and establishing a labeled sample; s2, generating and sorting a sample: extracting a small graph sample by taking megakaryocyte as a center; s3, constructing a deep learning model: constructing and training a deep convolutional neural network, and performing model optimization and performance improvement by using the generated small image sample set; s4, large image detection and reasoning: calling the trained model for reasoning by adopting a sliding window mechanism, and fusing detection results of a plurality of small windows back to an original large image through confidence weighting and a non-maximum suppression strategy; and S5, target cell segmentation: carrying out target segmentation on the detected megakaryocyte, and introducing a pyramid structure for cells with different sizes to obtain an accurate segmentation mask of each target cell. According to the method, the megakaryocyte detection and segmentation precision is improved through large image labeling, small image training and a sliding window reasoning strategy.

The invention discloses a semi-supervised medical image segmentation method based on causal uncertainty decomposition, and belongs to the field of medical image processing and artificial intelligence. According to the method, a segmentation model of teacher and student architectures is constructed, and a causal uncertainty decomposition module, a self-adaptive consistency learning module and a topology perception consistency loss module are integrated. The total uncertainty is decomposed into cognitive uncertainty and random uncertainty, so that targeted processing is realized; a dual-path weight fusion and differential modulation strategy is adopted to realize pixel-level adaptive learning; and a Betti number is introduced to calculate a topological distance, so that the integrity of an anatomical structure is kept. According to the method, under the condition that only 5%-20% of annotation data is used, the Dice coefficient on multiple medical image data sets is increased by 3.2%-4.8%, the segmentation precision and the boundary positioning accuracy are remarkably improved, the segmentation problem under the condition that medical image annotation is scarce is effectively solved, and the method has important clinical application value.

The invention discloses a detection method and system for non-inductive warehouse goods in and out of a warehouse, and the method is characterized in that the method comprises the following steps: collecting image information of an electric power fitting warehouse, and obtaining a to-be-detected image through the work of image labeling, image preprocessing, image enhancement and the like; analyzing the to-be-measured image through a polygon approximation method, and calculating the contour of the to-be-measured target; extracting a feature map by using a neural network, and identifying a to-be-detected target by using a coding and decoding method so as to obtain a plurality of parallax transformation modules; through the output of the plurality of parallax conversion modules, the to-be-detected image carries out non-inductive detection on the goods in the electric power fitting warehouse.

An AI auxiliary image annotation method based on semi-supervised learning comprises the following steps: acquiring a to-be-annotated image, extracting multilayer features of the image by using a deep learning network, and generating a feature map; the global context information and the local feature information are fused, and enhanced feature representation is generated and used for contour prediction; on the basis of enhanced feature representation, modeling a relationship between vertexes by adopting a graph convolutional network, and constructing a dynamic graph structure; according to the dynamic graph structure, calculating self-adaptive matching weights among the vertexes to realize a dynamic vertex pairing strategy; predicting an initial contour position by combining global and local features through a dynamic vertex pairing strategy; based on the predicted initial contour, a deformable convolutional network is adopted to realize progressive optimization of the contour; in the progressive optimization process, vertex position information is updated in real time, and the graph structure relation is adjusted; and finally, outputting an accurate object contour labeling result, and completing an image labeling task.

The embodiment of the invention relates to a multi-round medical image annotation method and device introducing a cross validation mechanism, and the method comprises the steps: transmitting a first image to a plurality of expert annotation interfaces, and merging the feedback annotation sets of all interfaces to obtain a first annotation set; setting cross validation items of every two annotation boxes in the first annotation set and constructing a corresponding cross validation matrix; on the basis of a cross validation matrix, dispute-free annotation box extraction, dispute-free annotation box aggregation and consistency score estimation are carried out; if the score is lower than a threshold value, a round of multi-person labeling is initiated again, otherwise, when the dispute labeling box set is empty, the non-dispute labeling box set serves as a final labeling result, when the dispute labeling box set is not empty, reexamination is conducted on the dispute labeling box set, and a set of the reexamination result and the non-dispute labeling box set serves as the final labeling result. According to the method, the labeling error of each image can be reduced, the overall labeling quality of all the images is improved, and the stability of the overall labeling quality is guaranteed.

The embodiment of the invention discloses an image auditing method and device based on a co-creation annotation mode, equipment and a medium. A specific embodiment of the method comprises the following steps: acquiring medical film and television data as initial data; preprocessing the initial data to obtain a task image set; distributing the task image set to at least one student terminal; in response to the detected annotation information sent by the student terminal, generating annotation feedback information according to the annotation information; sending the labeled feedback information to the student terminal; in response to the received annotation task image submitted by the student terminal, performing preliminary quality inspection on the annotation task image; sending the preliminary quality inspection result to an expert end; and in response to received auditing information corresponding to the preliminary quality inspection result sent by the expert terminal, sending the auditing information to the student terminal. According to the embodiment, a co-creation annotation mode is combined with a deep learning technology, so that efficient, accurate and high-quality image annotation auditing is realized, and the overall quality of annotation data is improved.

The invention relates to the technical field of brain tumor image segmentation, in particular to a brain tumor segmentation method and system based on anatomical perception symmetric comparison and cross-modal migration. The method comprises the following steps: carrying out data preprocessing on acquired multi-modal MRI image data; constructing a brain tumor segmentation model based on anatomical perception symmetric comparison and cross-modal migration; performing model training based on a two-stage decoupling training strategy; and performing model reasoning by using the trained model, and outputting a brain tumor segmentation result. Through a self-supervised learning framework, pre-training is carried out by using unmarked MRI data, dependence on a large-scale marked data set is greatly reduced, the problems of time consumption and high cost of medical image marking are solved, and the applicability of a model in a limited data scene is improved.

The invention discloses a semi-supervised image annotation method based on a limited label data set, and the method comprises the steps: taking a FixMatch frame as a basis, and integrating a learnable batch normalization module, a dual-scale parallel convolution module, a content and style separation dual-branch module and a dynamic residual gating module in a ResNet backbone network, the stability of feature extraction and the adaptive capacity to enhanced disturbance are improved. For a label-free sample, a multi-level pseudo-label fusion mechanism is provided, prediction distribution of weak, medium and strong enhanced views is synthesized, and high-confidence pseudo-labels are generated through confidence weighted fusion of multi-level enhanced views and comparison and screening with a category threshold. On the basis, a joint loss function composed of label supervision loss and pseudo label consistency loss is constructed, and a plurality of key control parameters in FixMatch + + are adjusted and optimized in a pre-experiment and grid search combined mode to obtain a group of optimal parameters of the model. Finally, a user inputs a label-free image into the trained FixMatch + + model, and the model can automatically generate a high-confidence pseudo label, so that the number of labeled samples in a limited labeled image set is increased, and the classification precision is improved. By implementing the method, the manual annotation cost can be reduced, and efficient and reliable support is provided for image analysis and recognition tasks.

The invention discloses an image annotation method, a client, terminal equipment and a storage medium, and relates to the technical field of image processing. The method is applied to a client and comprises the following steps: after a to-be-labeled image is rendered to a target container, calculating the scaling of the to-be-labeled image before and after rendering and the offset of the to-be-labeled image relative to the target container after the to-be-labeled image is rendered to the target container; performing target detection on the to-be-labeled image based on a preset target detection model to obtain a target detection result of the to-be-labeled image; correcting the target detection result through the offset and the scaling to obtain target annotation information of the to-be-annotated image; and according to the target annotation information, annotating the to-be-annotated image rendered into the target container. According to the invention, the accuracy of client image annotation can be improved.

The invention discloses an information processing method and device for fish hemorrhagic disease detection, equipment and a medium, and relates to the technical field of intelligent agriculture, and the method comprises the steps: obtaining a first healthy fish image and a second healthy fish image, converting the second healthy fish image into a diseased fish image through a cyclic generative adversarial network, and obtaining a diseased fish image; constructing an initial training set by using the healthy fish image and the diseased fish image; labeling the fish individuals in the initial training set by using an image labeling model, and obtaining a target training set obtained after the expert performs health state classification on the fish individuals in the labeled training set; and training the initial image detection model based on the target training set to obtain a to-be-detected fish image, and performing hemorrhagic disease detection on fishes in the to-be-detected fish image by using the target image detection model. A healthy fish image sample is converted into a diseased fish image through the cyclic generative adversarial network, and the problem that the model cannot be trained due to insufficient abnormal samples is solved.

The invention discloses a field core data feature automatic identification and extraction method and system. The method comprises the following steps: S1, acquiring a field core image and preprocessing the field core image; s2, performing image labeling on the preprocessed core image to obtain image classification and core attribute description; s3, training a core target detection and classification model by using the marked image data; s4, training a rock core attribute identification model by using a rock core target detection and classification result and rock core attribute information data; and S5, based on the trained core target detection and classification model and the core attribute identification model, carrying out target detection and classification on the core image, and carrying out attribute feature identification on a detected target object. According to the method, automation and intelligentization of the whole process from data collection, preprocessing and feature extraction to model training and application are realized, manual intervention is greatly reduced, and the working efficiency is improved.

The invention discloses a man-machine collaborative remote sensing image intelligent labeling method and device based on a visual large model. The method comprises the steps of obtaining remote sensing image data to be labeled, and performing image enhancement processing to obtain enhanced remote sensing image data; inputting the enhanced remote sensing image data into a visual large model for mask segmentation processing, and outputting a plurality of target segmentation masks and corresponding confidence coefficients; marking layer graphs with different colors on the to-be-marked remote sensing image data to form marked remote sensing image data, and displaying the marked remote sensing image data to a user; and the user performs confirmation operation, and stores the labeled remote sensing image data as a GeoTIFF file format image with geographic reference information. In the embodiment of the invention, the image labeling precision of complex ground objects, multiple regions and the like is obviously improved.

The invention relates to an interactive image annotation system and method based on facial posture and eye movement tracking and positioning, and solves the problems of poor annotation precision and low efficiency caused by the fact that two interactive technologies of facial posture detection and eye movement tracking are not organically combined with an automatic image segmentation algorithm in the existing image data annotation field. According to the invention, independent operation of two interaction modes of face posture detection and eye movement tracking is realized through modular design, a proper interaction mode is selected by judging the complexity of a scene, the subsequent processing time can be saved, and through secondary sliding mean filtering processing, the detection accuracy is improved. Errors caused by data noise of a depth camera or near-infrared eye movement tracking equipment based on iris reflection and micro movement of an operator are avoided, and the filtered stable coordinates are matched with a segmentation model of the interaction control module to carry out fine extraction on a target area. A traditional segmentation model and operator interaction information are organically combined in the interaction control module, and the manual labeling process is greatly simplified.

The invention discloses a three-dimensional interactive segmentation method and device, computer equipment and a storage medium, and is applied to the technical field of computers, and the method comprises the steps: obtaining a three-dimensional fluorescent microscopic image and interactive prompt information; wherein the interactive prompt information comprises a positive sample point, a negative sample point and a preorder segmentation mask; inputting the three-dimensional fluorescent microscopic image and the interactive prompt information into a constructed interactive segmentation model, and outputting a segmentation result; based on feedback of a user on the segmentation result, performing iterative optimization on the interactive segmentation model until the segmentation result meets preset precision; according to the invention, based on deep learning, the interaction prompt information and the three-dimensional fluorescence microscopic image are fused, accurate segmentation of the neuron structure is realized, and the precision and speed of neuron image labeling are improved.

The invention discloses a skeletal muscle ultrasonic image processing system and method for ultrasonic detection, and belongs to the technical field of medical ultrasonic image processing.The skeletal muscle ultrasonic image processing method comprises the steps that after a skeletal muscle ultrasonic image is subjected to non-local mean value dynamic filtering denoising, the contrast ratio of the skeletal muscle ultrasonic image is enhanced through nonlinear gray level transformation, and the skeletal muscle ultrasonic image is obtained; skeletal muscle areas are segmented by using the spatial continuity and similarity of skeletal muscles; on the basis of the segmented skeletal muscle region, carrying out binaryzation on a skeletal muscle ultrasonic image, calculating fractal dimensions by utilizing grid sliding statistics and weighting processing, constructing and smoothing a tensor model, calculating a main characteristic value and a characteristic vector, and positioning a skeletal muscle atrophy region through threshold judgment and clustering analysis; and performing labeling post-processing on the skeletal muscle ultrasonic image to generate a skeletal muscle ultrasonic image labeling report. The objective of the invention is to solve the technical problems of denoising, contrast enhancement, region segmentation, texture complexity quantification, atrophy region positioning, annotation report generation and the like of skeletal muscle ultrasonic images.

The invention provides a remote sensing image pixel-level labeling method and device, and relates to the technical field of remote sensing, and the method comprises the steps: obtaining a remote sensing image and positive and negative click coordinates inputted by a user, and inputting the remote sensing image and the positive and negative click coordinates into a trained image labeling model; and obtaining a pixel-level annotation mask for the remote sensing image output by the trained image annotation model. According to the remote sensing image pixel-level labeling method provided by the invention, the image information of the remote sensing image and the click information input by the user are fully interacted, and the labeling precision of the remote sensing image is improved.

The invention provides an image annotation method and device. The image annotation method comprises the following steps: acquiring image data including traffic control facilities; target detection is carried out on the traffic control facilities in the image data through multiple preset models, the detection result of the traffic control facilities output by each preset model is obtained, and the preset models comprise visual language models; performing target matching on the detection results output by the plurality of preset models to obtain a target matching result; determining an association rate index according to the target matching result and the detection result of each preset model, wherein the association rate index is used for evaluating the consistency of the detection results among the plurality of preset models; and under the condition of determining that the detection results output by the plurality of preset models meet a consistency condition according to the association rate index, performing tagging processing on the detection results output by the plurality of preset models according to the target matching result, and obtaining tag information of the image data.

The invention discloses an image annotation method and device and electronic equipment, and the method comprises the steps: determining a first image group and a second image group, the first image group comprises a first depth image and a first color image, and the second image group comprises a second depth image and a second color image; the first image group is an image group obtained by shooting a first scene based on a first shooting angle, the second image group is an image group obtained by shooting a second scene based on the first shooting angle, and the second scene is a scene obtained by adding a target object in the first scene; determining a first area according to the first depth image and the second depth image; and marking the second color image according to the first area. Therefore, the electronic equipment can automatically label the color image based on the two image groups obtained by shooting the first scene and the second scene obtained by adding the target object in the first scene, manual labeling of the image is not needed, the labor cost is greatly saved, and the labeling efficiency is improved.

The application provides a lung CT image processing method and system based on multi-scale space semantic fusion, which comprises the following steps: collecting multiple lung CT-DICOM images, and performing quality inspection processing and format conversion to obtain original sample images; pre-processing and lung parenchyma segmentation are performed on the original sample images to obtain standard sample images; positive and negative samples are determined according to the image labeling results, and the positive and negative samples are subjected to random interference to obtain expanded sample images; a training data set is generated based on the expanded sample images and the standard sample images; the YOLOv12 network is improved based on the MSSF module and the C3K2_MSSF module to obtain an improved YOLOv12-3D network, and the improved YOLOv12-3D network is trained by using the training data set to obtain a target detection model; and the CT image to be detected is input into the target detection model to obtain detection information. The improved YOLOv12-3D network architecture realizes deep fusion of three-dimensional space features and multi-scale space semantic features in the feature extraction stage, and significantly improves the extraction rate and efficiency of small targets.

This specification provides an image annotation model training method, image annotation method, and apparatus. The image annotation model training method includes: determining initial training data for an image annotation task, wherein the initial training data includes a training image, annotation instructions corresponding to the training image, and target region locations corresponding to the annotation instructions; identifying the initial training data to determine text data and multi-granularity location information of the text data, wherein the multi-granularity location information includes location information of at least two different text dimensions; constructing target training data based on the initial training data, the text data, and the multi-granularity location information; and training the initial image annotation model using the target training data and the target region locations to obtain a target image annotation model, wherein the target image annotation model is used to perform the image annotation task.

Disclosed are an image labeling method and device, a storage medium and equipment, wherein the method comprises: obtaining a three-dimensional point cloud of a target scene and at least one to-be-labeled image sequence, labeling static elements in the three-dimensional point cloud of the target scene to obtain labeling information of the static elements in the three-dimensional point cloud, and then projecting the labeling information of each static element in the three-dimensional point cloud to at least one to-be-labeled image set corresponding to the target scene, thereby realizing labeling of static elements in the at least one to-be-labeled image set corresponding to the target scene. Based on the embodiments of the present disclosure, labeling of static elements in all to-be-labeled images in the at least one to-be-labeled image set corresponding to the target scene can be realized at one time, and compared with labeling of to-be-labeled images corresponding to different orientations of the target scene one by one, the image labeling efficiency can be effectively improved.

The utility model provides a keyboard circuit for remote sensing image labeling, which relates to the technical field of keyboard circuits and comprises a break point moving key, a function control key and a single chip microcomputer used for controlling terminal equipment to perform remote sensing image labeling according to input commands of the break point moving key and the function control key. One end of the break point moving key and one end of the function control key are electrically connected with the input end of the single-chip microcomputer, the other end of the break point moving key and the other end of the function control key are electrically connected with the grounding end of the single-chip microcomputer, and the output end of the single-chip microcomputer is connected with the terminal equipment. According to the utility model, the labeling operation is effectively simplified, and the label manufacturing speed and the marking accuracy of the folding point position are improved.

The invention belongs to the field of image processing and computer vision, and relates to an object-level contrast learning method for multi-modal target detection. The invention provides an object-level intra-modal and cross-modal combined contrast learning method aiming at the problems that multi-modal remote sensing image labeling cost is high, a pre-training structure and a detection task are not matched, and complementary information among modals is insufficient in utilization. The method comprises the following steps: obtaining and preprocessing paired visible light and infrared images, and generating and screening candidate boxes; performing multi-view enhancement on the two-mode image and synchronously mapping a proposal box; a double-branch pre-training network is constructed, object features are extracted in a multi-level mode in a feature pyramid, and target network parameters are updated through intra-modal and cross-modal comparison loss joint optimization and index moving average. And after pre-training is completed, migration to a detection model is carried out, multi-modal fusion detection is realized through fine adjustment of a small amount of annotation data, the detection precision and robustness can be improved, and annotation dependence is reduced.

The application discloses a coal mine transportation belt deviation detection method based on optical flow detection and end-to-end, and belongs to the technical field of safety monitoring. In view of the problems that the prior art cannot realize real-time monitoring on the running condition of the belt, the inspection efficiency is low, and there is a certain lag, a data set is made by collecting a coal mine transportation video, a carrier roller is labeled with the aid of image labeling software, and target frame position information of the carrier roller is calculated by using a carrier roller detection model, a slope of a belt transportation direction is calculated by using an optical flow detection algorithm, a straight line is determined according to the slope and the target frame position information of the carrier roller, the target frame of the carrier roller is divided into left and right regions, an edge graph obtained through edge detection and the target frame of the carrier roller with the divided position are used to generate a mask mask, an interested region is extracted, straight line information in the edge graph is processed in combination with straight line detection, position information of the belt and the carrier roller is determined, and whether the belt deviates is judged.

The invention discloses an image annotation automatic quality inspection method, and relates to the technical field of image annotation quality inspection, and the image annotation automatic quality inspection method mainly comprises the steps: processing original data to obtain training data; constructing a pseudo-twinning neural network, and training the pseudo-twinning neural network by using the training data and the loss function to obtain a quality inspection model; and predicting the image annotation data to be subjected to quality inspection by using the quality inspection model to obtain a quality inspection score, and endowing a quality inspection result to the image annotation data to be subjected to quality inspection. By implementing the image annotation automatic quality inspection method provided by the invention, the accuracy, quantification and efficiency of image annotation automatic quality inspection can be improved.

The invention provides a medical image intelligent labeling and auditing method based on deep learning, and relates to the technical field of medical image labeling, and the method comprises the steps: carrying out the preprocessing of medical image data, and obtaining the preprocessed image data; performing two-dimensional evaluation on the preprocessed image data based on image complexity and labeling task complexity to obtain a total complexity score; dividing the preprocessed image data into a simple level, a medium level and a complex level according to the total complexity score; carrying out labeling processing on the preprocessed image data by adopting a differential labeling strategy to obtain a labeling result; performing quantitative evaluation on the labeling result to obtain a quality score; extracting labeling process features, and dividing labeling results into high quality, medium quality and low quality; and determining an auditing strategy, auditing the annotation result, and outputting the annotation result which is audited to be qualified. According to the method, objective evaluation of labeling quality and reasonable configuration of auditing resources can be realized, and the consistency of auditing is guaranteed.