200 results about "Region proposal" patented technology

The invention relates to the technical field of image processing, and discloses an intelligent machine vision detection method and system based on image processing, and a storage medium. The method comprises the steps of performing candidate region generation and geometric constraint optimization processing on fusion features through a cascade bidirectional region suggestion network to obtain a target candidate region, and performing instance segmentation processing on the target candidate region through a graph convolutional network and a boundary dynamic adjustment mechanism to obtain a segmentation result; performing feature extraction processing on the segmentation result through a space-time joint attention network to obtain space-time context features; performing multi-target tracking processing on the spatio-temporal context features through depth correlation measurement and motion mode matching to obtain a trajectory prediction result; and performing abnormal mode modeling processing on the trajectory prediction result through hierarchical contrast learning and multi-modal feature fusion to obtain abnormal features, and generating a visual inspection report according to the abnormal features. According to the invention, the accuracy of intelligent machine vision detection based on image processing is improved.

The invention relates to the technical field of image processing, can be applied to business scenes of medical health, financial science and technology and the like, and discloses an image sensitive character desensitization method which comprises the following steps: extracting a plurality of feature maps from a to-be-processed image, and inputting a feature pyramid network to generate pyramid features, fusing a spatial attention mechanism to enhance the response of a sensitive region in each level of feature map, generating candidate region coordinates through a region candidate network, mapping the candidate region coordinates to a to-be-processed image to obtain a local image, extracting text semantic information in the local image by using a codec based on a self-attention mechanism, and obtaining a local image; and performing sensitive word detection in combination with the semantic classification model, and performing local texture repair operation on the sensitive area according to a detection result to generate a desensitized image. According to the method, a space attention mechanism and a semantic understanding module are introduced on the basis of multi-scale feature fusion, so that the capability of detecting small-size sensitive characters in an image is improved.

One embodiment of the present invention sets forth a technique for extracting data from a document. The technique includes determining a first set of features associated with the document, wherein the first set of features comprises a set of region proposals that bound one or more portions of text within the document. The technique also includes applying a first machine learning model to the first set of features to generate a set of predictions associated with one or more key-value pairs included in the document. The technique further includes extracting the one or more key-value pairs from the document based on the set of predictions.

The invention belongs to the technical field of target classification, and particularly relates to a three-dimensional point cloud data analysis method and system based on artificial intelligence. Comprising the steps of data acquisition, data preprocessing, model construction, model training, model prediction and the like. The integrity and precision of point cloud data are effectively improved by collecting original point cloud and performing intelligent denoising and complementation operation; constructing a depth model by adopting an input layer, a region proposal layer and a classification and regression layer, and carrying out accurate classification and three-dimensional bounding box prediction on a target; wherein the region proposal layer is combined with a seed point feature extraction and voting mechanism to generate a candidate region, a classification branch outputs a category probability, and a regression branch predicts bounding box parameters. The system adopts a modular design, has the advantages of strong noise suppression, high complementation precision, high target detection accuracy, multi-scene applicability and the like, and is particularly suitable for efficient automatic identification of a complex three-dimensional structure.

The invention discloses an electronic device detection system based on computer vision, and relates to the technical field of electronic device automatic detection. The system comprises an image processing module, a target detection module and a defect identification module. The image processing module extracts a feature map of the electronic device image through a convolutional neural network. And the target detection module performs candidate region generation on the feature map through a region proposal network, performs binary classification analysis on each anchor frame, and outputs classification confidence. And for the anchor frames classified as targets, the positions and sizes of the anchor frames are optimized through boundary regression, redundant anchor frames are removed through non-maximum suppression, and target areas conforming to confidence are screened out. And the defect identification module is used for carrying out image block division on the target area, carrying out defect type classification in combination with a self-attention mechanism, identifying different defect modes, carrying out boundary regression optimization on the defect area, and outputting the position, the type and the confidence coefficient of the defect. Defects in the electronic device can be efficiently and accurately detected.

A method may include obtaining a petrographic image. The method may further include determining various region proposals based on the petrographic image and a selective searching function. A respective region proposal among the region proposals may correspond to various pixels in the petrographic image according to a predetermined dimension. The method may further include determining color histogram data for the petrographic image. The method may further include determining input image data based on the petrographic image, the region proposals, and the color histogram data. The method may further include determining a rock object using the input image data and a machine-learning model.

The invention discloses a three-dimensional shielding target tracking method based on multi-modal space-time interaction, and relates to the technical field of target tracking. The method comprises the following steps: acquiring a point cloud and an image and preprocessing to obtain global fusion features; obtaining an initial detection frame and region-of-interest features through region proposal network processing; projecting the non-empty voxel point cloud to the image features, and reconstructing shielded target features; convolution and neural network processing are utilized to obtain a refined detection frame; screening legal detection frames through distance calculation and legality judgment; the bipartite graph and the self-adaptive channel graph are adopted for convolution, and appearance correlation scores are calculated; and matching the detection frame and the trajectory based on a Hungary algorithm to realize whole-course tracking. The target identification accuracy and robustness are improved, the shielding problem is solved, the accuracy of the detection frame is ensured, the correlation accuracy is improved by using the bipartite graph and the adaptive convolution, the nodes are matched in combination with the geometric cost matrix, and whole-course tracking and error calibration are realized.

The invention belongs to the technical field of computer vision and image processing, and discloses a remote sensing small sample target detection method based on double-attention guided transfer learning, and the method comprises the steps: obtaining a remote sensing image data set, and carrying out the preprocessing; taking the preprocessed remote sensing image training set as input, constructing a basic detection model by using a ResNet-101 backbone network, a feature pyramid network and a content awareness upsampling and regional proposal network, and obtaining basic model parameters; basic model parameters are used as input, a DA-FSDET network is trained based on a content awareness strip pyramid and a deformable attention area proposal network, and the trained DA-FSDET network is used to acquire a category detection frame containing small sample categories and confidence. Through cascading and cooperative work of the content awareness stripe pyramid and the deformable attention area proposal network, the detection precision and robustness of the multi-scale target in the remote sensing image are effectively improved.

The invention relates to the technical field of AI visual target detection, and discloses an AI visual target detection method based on multi-modal feature fusion. According to the method, visual image data and spatial point cloud data are acquired through visual acquisition equipment and laser radar equipment, and after timestamp alignment and resolution unification, visual feature data containing edge contours and spatial feature data containing position distribution are extracted respectively. Inputting the two types of features into a feature fusion model, distributing weights according to the feature association degree, the texture complexity and the position dispersion, and performing feature alignment and attention mechanism processing to obtain fusion features; and then region proposing, category classification and position regression are carried out through a target detection model, after down-sampling, candidate region generation and non-maximum suppression are carried out, the confidence is adjusted in combination with historical trajectory information, and finally a high-confidence result is converted to an original image space and output. According to the method, multi-modal features are effectively fused, and the precision and reliability of target detection in a complex scene are improved.

A method for operating a computing system on at least one processor includes performing search space reduction on input data using a first trained artificial intelligence model to generate relevant regions in the input data. The method also includes generating region proposals in the relevant regions using a second trained artificial intelligence model. The method further includes performing unsupervised anomaly classification on the region proposals using a third trained artificial intelligence model to classify each of the region proposals as normal or as an anomaly. The method further includes performing contextual filtering on the region proposals classified as anomalies to determine if any of the region proposals classified as anomalies are contextually normal using a fourth trained artificial intelligence model.

The invention provides an unmanned aerial vehicle target detection method based on long-tail distribution optimization. The method comprises the steps of performing data fusion on acquired multi-modal data; inputting the fused data into a feature extraction network for feature extraction, and performing adaptive reweighting of channels and spaces during feature extraction; dynamically adjusting an anchor box according to the extracted feature map by adopting a regional proposal network to generate a candidate box; calculating a joint confidence coefficient according to the candidate box and the feature map so as to optimize a sampling strategy of a category bias sampler; respectively processing the sampled head feature frame and the tail feature frame to obtain a corresponding category prediction probability and a bounding box offset, and decoding a prediction depth value by adopting a depth decoder according to the candidate frame and the feature map; according to the category prediction probability, the bounding box offset and the prediction depth value, constructing a total loss function to perform model training; outputting a detection result by adopting the trained target detection model; therefore, the detection sensitivity of low-frequency and high-risk targets in the construction site is remarkably improved.

The invention discloses a target detection system and method based on computer vision, and relates to the technical field of vision technologies. Comprising a data acquisition module used for acquiring image or video data; the data preprocessing module is used for cutting, zooming, normalizing and enhancing the collected data; the feature extraction module is used for extracting feature information in the image; the target detection module is used for generating candidate target regions and carrying out classification and bounding box regression on the candidate regions in combination with a region suggestion network and a classifier; the multi-modal data fusion module is used for fusing the RGB image data and other modal data so as to improve the accuracy and robustness of target detection; the post-processing module is used for performing non-maximum suppression, confidence threshold screening and result visualization processing on the detection result; according to the invention, the accuracy and real-time performance of target detection are significantly improved; the robustness to small targets and complex scenes is improved; and the development cost and maintenance difficulty of the algorithm are reduced.

This invention discloses a SAR target detection and recognition method based on a multi-level enhancement network. This method primarily addresses the problems of existing technologies in complex environments, such as poor robustness, high false alarm and missed detection rates, and low detection and recognition accuracy. The method involves labeling and partitioning SAR measured data to obtain training and test sets; constructing a multi-level enhancement network consisting of a cascade of data-level enhancement modules, feature-level enhancement modules, region proposal modules, and decision-level enhancement modules; training the multi-level enhancement network using the training set based on a stochastic gradient descent algorithm; and inputting the test set images into the trained multi-level enhancement network to obtain SAR target detection and recognition results. This invention significantly improves SAR target detection and recognition performance in complex environments and can be used for battlefield reconnaissance and situational awareness.

The invention discloses an intelligent identification and positioning method for an overhead line system cantilever insulator, and the method comprises the steps: obtaining an insulator image, and carrying out the preprocessing of the image; constructing a Faster R-CNN network, and training the network to obtain a feature model; inputting the obtained image into the feature model to obtain pixel coordinates of the insulator; on the basis of the obtained pixel coordinates, the world coordinates of the insulator are obtained by analyzing the mapping relation between the pixel coordinates and the world coordinates; the Faster R-CNN network comprises a region proposal network and a Fast R-CNN target detection network, and the region proposal network and the Fast R-CNN target detection network share a full graph convolutional layer. The method is formed by combining the two convolutional networks, the whole detection process is more efficient, high-resolution images can be processed in a shorter time, and the high requirement of modern railway maintenance for real-time monitoring is met.

The invention discloses an irregular text recognition method based on deep learning and Raspberry Pi, and the method comprises the steps: firstly carrying out the data preprocessing, inputting an image and a corresponding text region label, and carrying out the standardization and data enhancement of the image; feature extraction is carried out through a backbone network, and an improved self-attention mechanism is added in front of an SPPF module, so that the quality of features is improved; performing text region proposal, inputting the extracted features into a text region proposal module, and outputting a predicted text region; identifying text content from the proposed text region using a sequence model; calculating the total loss of the text region detection loss and the text recognition loss; in the training process, two kinds of precision FP16 and FP32 are used for calculation; and finally, deploying an algorithm at a Raspberry Pi terminal to solve a real-time scene irregular text recognition task. And the performance of the whole text detection and recognition system can be remarkably improved, so that the text detection and recognition system has higher accuracy and robustness in various complex scenes.

The invention discloses an aerial target tracking method based on multi-frame fusion, and belongs to the technical field of target tracking, and the method comprises the steps: carrying out the preprocessing of an image based on an image correction model, and obtaining a first feature set through combining with a pre-trained deep convolutional neural network model; obtaining a first motion track based on the first feature set; if the deviation between the first motion track and the target position in the actual frame image exceeds a preset threshold value, obtaining an auxiliary data set and combining the auxiliary data set with the first feature set to obtain a second feature set; based on the second feature set, adopting a feature enhancement algorithm based on an attention mechanism to obtain a third feature set; and a continuous tracking state of the target is obtained based on the third feature set, and if the target is lost, a target re-identification algorithm based on a regional proposal network is adopted to obtain a recovered target tracking state, and a tracking optimization algorithm based on an adaptive threshold is adopted to carry out optimization. According to the method, complex illumination and shielding can be effectively dealt with, and the accuracy, robustness and continuity of multi-target tracking are improved.

The invention relates to a generalized few-sample target detection method based on differential multi-scale fusion. The method comprises the following steps: constructing a few-sample target detection model; the few-sample target detection model comprises a multi-scale feature fusion module and a difference convolution fusion module; generating a few-sample initial region proposal according to the region proposal network; a corresponding mask area is extracted from the initial area proposal, proportional expansion optimization is carried out, a target area is input into a multi-scale feature fusion module, features of the target area are extracted through five different-scale convolution kernels, and attention weights are generated through a scale attention mechanism to carry out adaptive weighted fusion; and inputting the fused multi-scale features into a differential volume fusion module, and obtaining final target features by establishing a directional sensitive differential feature map of a pixel neighborhood and fusing the directional sensitive differential feature map with an original feature map. By adopting the method, accurate positioning of object boundaries and effective reservation of object global information can be realized, and optimal balance extraction of local details and global semantic information can be realized at the same time.

The invention relates to a weather modification work target cloud layer identification method, system and device and a medium, and belongs to the technical field of weather cloud layer identification, and the identification method comprises the steps: obtaining original cloud layer image data of a target cloud layer region, carrying out the preprocessing, and generating a standardized feature image; performing multi-level feature extraction on the standardized feature image based on a feature extraction network to generate a feature map; traversing the feature map based on a region suggestion network, and generating an initial detection result; performing feature alignment processing on the bounding box of the candidate region based on a region-of-interest pooling algorithm, and outputting feature vectors of the candidate region to a classification regression network to obtain a detection result set; performing full convolution segmentation operation on each candidate region in the detection result set to generate a pixel-level segmentation mask; the cloud cover proportion, the cloud layer category, the texture morphological characteristics and the vertical height parameters of the target cloud layer area are calculated respectively, and a cloud layer characteristic parameter set is constructed and obtained; the cloud feature recognition accuracy can be improved.

One embodiment of the present invention sets forth a technique for performing a labeling task. The technique includes determining one or more region proposals, wherein each region proposal included in the one or more region proposals includes estimates of one or more bounding boxes surrounding one or more objects in a plurality of video frames. The technique also includes performing one or more operations that execute a refinement stage of a machine learning model to produce one or more refined estimates of the one or more bounding boxes included in the one or more region proposals. The technique further includes outputting the one or more refined estimates as initial representations of the one more bounding boxes for subsequent annotation of the one or more bounding boxes by one or more users.

The invention discloses an image and point cloud fusion multi-modal three-dimensional target detection method, which comprises the following steps: dynamically establishing bidirectional mapping of point cloud and voxels in a Bev view angle based on a voxelization method of double hash tables, and then extracting geometric features of each Bev voxel; after the point cloud is converted from a LiDAR coordinate system to an image coordinate system, the reflection intensity of the point cloud serves as a new channel to be attached to an image, four-channel image tensor representation is obtained, and global multi-level image semantic features are obtained through an adaptive feature extraction module based on perspective voxels; obtaining a regional semantic feature corresponding to the perspective voxel geometric feature through a perspective voxel projection method, and achieving the correlation alignment of the regional semantic feature and the point cloud geometric feature; a channel weight adaptive module based on a cross attention mechanism is adopted to weight geometric features and semantic features respectively, the weighted features are spliced to generate a feature tensor, and then a region proposal RPN network is adopted to carry out classification and regression tasks.

The invention relates to a Faster Rcnn-S-based small target detection method, and belongs to the field of image processing technologies and computer vision. The method comprises the following steps: acquiring a small target detection data set, performing data enhancement on the data set, and dividing the data set into a training set and a test set; a small target detection model based on Faster Rcnn-S is constructed; according to the model, a Faster Rcnn network is used as a basic network, a multi-scale decoupling large-kernel convolution mixed attention module is added, a two-stage feature pyramid network with feature enhanced alignment is adopted for feature fusion, a multi-stage region suggestion network structure is introduced, and a fine-grained decoupling detection head is designed to replace a common target detection head; training the model by adopting the training set to obtain a trained model; and inputting images in the test set into the trained model to obtain a target detection result. According to the invention, the accuracy of small target detection can be improved.

The invention discloses a remote sensing target detection method for an open set scene, and belongs to the technical field of open set target detection. The open set remote sensing target detection network introduces a cascade target positioning network, a background feature enhancement module and reciprocal point prototype learning on the basis of a Faster-RCNN model; the cascade target positioning network can effectively adapt to multi-scale features, calculates the quality of a candidate box through centrality loss, and replaces a foreground-background classification mode in a traditional region suggestion network, so that the recall rate of objects is increased, and the possibility that unknown objects are misjudged as backgrounds is reduced; the background feature enhancement module fuses various background region information so as to enhance the adaptability to a complex environment and reduce the interference of background noise on target recognition; according to reciprocal point prototype learning, a reciprocal point prototype of a known category and a background is constructed, and an embedded network metric learning strategy is combined, so that background information and the known category jointly participate in candidate box classification, and a known target, an unknown target and the background are effectively distinguished.

The invention discloses a tiny target detection method and system based on a hybrid enhancement network, and the method comprises the steps: taking a remote sensing image as the input of a target detection network, carrying out the preprocessing and multi-stage feature processing of the remote sensing image through a backbone network, extracting the multi-scale texture features and global context features, and carrying out the recognition of the features; obtaining fusion feature maps of different stages; inputting the fused feature map into a feature pyramid network and a region proposal network in sequence to generate a tiny target candidate box; extracting fixed-size features from the candidate box, inputting the fixed-size features into a mixed attention enhancement module, and learning context-enhanced tiny target feature representation; the prototype library is dynamically updated by using the prototype comparison loss of the prototype comparison learning module, the target detection network is optimized, tiny target classification and positioning are completed through a directional detection head, and a detection result is output; the method has the advantages of strong feature extraction capability and high robustness.

The invention discloses an improved Faster R-CNN underground drainage blow-off pipeline defect detection model, and particularly relates to the field of target detection, and the method comprises the steps: firstly constructing a defect image data set, extracting a defect-containing image from a pipeline detection image, and dividing the defect-containing image into a training set, a verification set and a test set according to a preset proportion after validity screening and defect category labeling; secondly, improving a detection model, replacing a traditional regional proposal module with an anchor-frame-free detection head, embedding an attention module in a high layer of a backbone network, and introducing a feature pyramid network to generate multi-scale features; then training the model, performing multi-graph splicing type data enhancement on a training set, training by adopting an optimizer and a two-stage loss function, and storing an optimal weight in combination with an early stop strategy; and finally evaluating the model, inputting the test set to obtain a detection result, counting related detection data, calculating a precision index, and comparing the precision index with a preset threshold to judge whether the model reaches the standard. The method is high in detection precision and high in adaptability, and meets the requirement for pipeline defect detection.

The invention discloses a 3D target detection method, a computer program product and a terminal, belongs to the technical field of image processing, and jointly calibrates a laser radar and an image acquisition device. Mapping the global feature of the point cloud to an image feature map by using a transformation kernel, constructing a sparse matrix, and flattening the image feature map into a matrix; performing batch processing and normalization processing on the sparse matrix and the matrix, and then performing connection processing to obtain fusion features of the image and the point cloud; performing region proposal by using the fusion features to generate candidate target regions; and carrying out classification and bounding box regression on the candidate regions, determining the category and the position of the target, and obtaining a 3D target detection result. The mapping relation between the point cloud and the monocular image is constructed by using the sparse point cloud, and it is ensured that only image pixel point coordinates and point cloud coordinate pairs sharing the same point can be paired, so that accurate alignment of features is realized, the accuracy of target detection is improved, and the precision of 3D target detection is ensured.

The invention discloses a cross-domain small sample target detection method based on dynamic information fusion, relates to the field of industrial defect detection and the like, and aims to solve the problems of inter-domain distribution difference, annotation scarcity and the like in a cross-domain small sample scene. The method specifically comprises the following steps: firstly, dividing a source / target data set, constructing a DIC-ViT model containing double branches, and sharing a DINOv2 backbone network to extract features; an anchor box is generated through a region proposal network, and sample division is optimized by combining dual criteria of an IoU value and a category center distance; then feature adaptive fusion is realized through a dynamic information coupling module, and a discriminative category prototype is generated through a contrast learning module; and finally, optimizing the model by using an objective function containing classification, regression and contrast loss. The method can improve the target detection robustness and precision, is excellent in performance in a multi-data-set test, and is suitable for a detection scene with scarce samples.

The invention discloses an embedded tooth root bone adhesion feature recognition method and system based on deep learning, and relates to the field of medical image recognition, the position of an embedded tooth is detected based on a normalized image, image cutting of the embedded tooth and the surrounding area thereof is carried out along the long axis of a tooth body, the cutting area is a three-dimensional rectangular frame, and the three-dimensional rectangular frame is used as a matrix; classifying the cut images according to FDI tooth positions; performing hierarchical slicing on the classified images along the long axis of the tooth, taking the images after hierarchical slicing as input of an embedded tooth root bone adhesion feature recognition model, extracting depth features of the images in a feature extraction network ResNet50, removing a final full-connection layer of the ResNet50 model, retaining a feature map output by a convolutional layer, and identifying the embedded tooth root bone adhesion feature in the feature extraction network ResNet50; and inputting a feature map output by the convolutional layer into the region suggestion network and the classification and regression head to obtain a feature recognition result, including the probability that the embedded tooth root bone adhesion feature prediction candidate region belongs to the target category and the accurate position of the embedded tooth root bone adhesion feature candidate region bounding box.

The invention discloses an unmanned aerial vehicle visual angle target detection method and system based on self-regularization point supervision, and the method comprises the steps: obtaining a to-be-detected target image through an unmanned aerial vehicle, generating a feature vector of a candidate region through a region proposal mechanism, and enabling the to-be-detected target image to be associated with a target level point label in the image; the method comprises the following steps: constructing a PointSR target detection network model by adopting a self-regularization sampling mechanism based on a target detection network of weak supervised learning; and based on a Point SR target detection network model, carrying out point supervision target detection on the feature vector of the candidate region to obtain a point supervision target detection result under the view angle of the unmanned aerial vehicle. According to the method, a double-flow structure is adopted to classify the candidate regions, so that effective weak supervision target detection is realized. The unmanned aerial vehicle visual angle target detection method and system based on self-regularization point supervision can be widely applied to the technical field of image supervision target detection.

The invention belongs to the technical field of artificial intelligence and remote sensing image detection, and particularly relates to a strip mine area remote sensing image target detection method based on a spatial relation constraint graph network. According to the method, firstly, an image is preprocessed, candidate target areas are extracted, and a graph structure reflecting the spatial distance and direction relation between targets is constructed; fusing the local visual features and the global spatial information by using a graph convolutional network to realize efficient extraction of target features; and finally, accurately positioning and identifying a mining area target through area suggestion, boundary regression and post-processing. According to the technology, interference caused by complex backgrounds and scale changes is effectively relieved, the detection precision and robustness are remarkably improved, reliable technical support is provided for mining area safety monitoring and resource management, and innovativeness and practical value are achieved.

A device includes a memory and a processor. The memory can be configured to store image data of an input image. The processor can be configured to detect one or more objects in the input image using a quantized multi-level object detection network, wherein quantization of the quantized multi-level object detection network includes (i) generating quantized image data by performing a first data range analysis on the image data of the input image, (ii) generating a feature map and a proposal bounding box by applying a region proposal network (RPN) to the quantized image data, (iii) performing a region of interest pooling operation on the feature map and a plurality of ground truth boxes corresponding to the proposal bounding boxes generated by the RPN, (iv) generating a quantized region of interest pooling result by performing a second data range analysis on the result from the region of interest pooling operation, and (v) applying a region-based convolutional neural network (RCNN) to the quantized region of interest pooling result.