132 results about "Region proposal" patented technology

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 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.

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 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.

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 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 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 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.

The application discloses a three-dimensional target detection method based on a multi-modal feature fusion of a Transform, and comprises the following steps: 1. collecting point cloud data by using a laser radar and sampling the point cloud data, and collecting image data by using a camera; 2. inputting the data collected by the laser radar and the camera into a multi-modal feature fusion RPN network based on a Transform to extract a region proposal frame; and 3. inputting the region proposal frame information into a refinement network to obtain a final prediction frame. The application can avoid the problems of false detection and missed detection in the target detection process based on the multi-modal feature fusion, so that the accuracy of the evaluation of the perceived environment can be ensured.

The invention relates to the technical field of computer vision and intelligent traffic, in particular to a road spilled object detection method and device based on a visual large model, and the method comprises the steps: firstly constructing multi-scale feature representation for an image, and obtaining a context enhancement feature map through a feature coding network containing a multi-head self-attention mechanism; generating a candidate region by a region suggestion network based on the feature map and giving a position parameter; performing instance separation processing in the candidate region to form instance-level candidate region features; and inputting the features into a classification sub-network and a boundary regression sub-network, and outputting a bounding box and a category label. According to the method, boundary continuity and positioning consistency are kept in close, overlapped and weak texture scenes, and the method is suitable for road safety monitoring and vehicle auxiliary identification.

The invention relates to the field of combination of traditional Chinese medicine and artificial intelligence, and discloses a tongue tracking method and device, terminal equipment and a storage medium, and the method comprises the steps: carrying out the tongue tracking of a current frame image through a basic tracker in response to the input current frame image, and outputting a first tracking result; under the condition that the first tongue body tracking result is verified to be unreliable, performing region suggestion generation and target image matching on the current frame image by utilizing a global search module so as to output the region suggestion with the highest matching degree as a target tracking result of the current frame; iteratively updating the tongue tracking model in the basic tracker by using the output tracking result; and continuously performing tongue tracking on the input next frame of image by using the basic tracker until the last frame of image is tracked. The method can realize continuous accurate tracking of the tongue body.

The application discloses a kind of dual paradigm combined remote sensing image end-to-end fine-grained target detection method, it is related to computer vision and pattern recognition technical field, including: extracting multi-scale feature map by feature extraction network;Based on sparse directional proposal network, using one-to-one sparse matching strategy, generate no duplicate sparse directional region proposal and its corresponding proposal feature and coarse-grained target confidence;Based on query perception refinement head, using one-to-many dense matching strategy, obtain fine-grained classification probability and refined bounding box;In the training stage, introduce double auxiliary supervision head to provide additional dense supervision signal;In inference stage, adopt two-stage consensus scoring mechanism, finally obtain fine-grained target detection result.The method realizes the end-to-end remote sensing image fine-grained target detection without NMS dependence, through the cooperation of sparse and dense dual paradigm, effectively improves the positioning accuracy and fine-grained classification performance.

A method of performing target detection during autonomous driving, comprising: performing 3D target detection in a 3D target detection segment; uploading outputs of a plurality of sensors in communication with the 3D target detection segment into a plurality of point clouds; transmitting point cloud data of the plurality of point clouds to a region proposal network (RPN); independently performing 2D target detection in a 2D target detector, the 3D target detection being performed in parallel in the 3D target detection segment; and obtaining a given input image and simultaneously learning bounding box coordinates and class label probabilities in a 2D target detection network that operates to treat target detection as a regression problem.

The present invention relates to the technical field of video image processing, and provides a method for detecting marked parking spaces based on semantic feature attention fusion. The method includes the steps of: a. using a backbone network to extract initial feature information of a parking space image; b. using a feature attention fusion module to perform feature fusion on the initial feature information to obtain fused features; c. using a Region Proposal Network (RPN) on the fused features to generate candidate region features; d. using an object detector to detect the candidate regions to directly obtain the four corner coordinate information of the marked parking space targets in the image. The method for detecting marked parking spaces based on semantic feature attention fusion provided by the embodiments of the present invention can effectively improve the average precision of marked parking space detection and has high robustness.

The present invention relates to the field of target detection in machine vision, and specifically to a semi-supervised object detection system based on distinguishable features and a training method thereof. The system includes: an input image feature extraction module, a distinguishable feature-sensitive region proposal module, a distinguishable feature extraction and storage module, a distinguishable feature data enhancement module, and an object recognition and positioning module; the training method includes: step 1: determining a target detection network and building the system as described above; step 2: collecting an application scenario data set, wherein the application scenario data set includes labeled data and unlabeled data; step 3: using the application scenario data set to train the system; step 4: iterating the previous step until the trained system model achieves an ideal object detection effect. Through the present invention, the model's ability to utilize data is improved, effectively solving the problem of over-reliance on labeled data and insufficient utilization of unlabeled data in semi-supervised learning.

The invention provides a multi-camera multi-unmanned aerial vehicle tracking method based on a global attention mechanism, relates to the technical field of video target tracking, and designs a GAM-MTMCT model containing GAM and Ghost convolution. Specifically, a feature extraction network based on ResNet-50 is constructed, a GAM attention mechanism module is introduced, a feature pyramid network FPN is combined with feature maps from different levels, and a region proposal network RPN is used to generate candidate regions on a multi-scale feature map generated by the feature pyramid network FPN; a region proposal network RPN generates candidate frames with different sizes and proportions by sliding a small window on the feature map, and calculates a score and a position regression value for each candidate frame; after the candidate areas are generated, the candidate areas are aligned, and target detection and classification are carried out on the candidate areas subjected to ROI (Region of Interest) Align processing by using Desection Head; and the Desection Head processes the candidate region through Ghost convolution, and outputs the category and position regression information of each candidate box.

A kind of construction method and device of depression recognition model based on deep network feature fusion, its method includes: data acquisition;Build the video dataset containing video regional level label;Respectively using C3D neural network and channel attention network to the whole video stream is reinforced feature extraction;The reinforced feature is used RPN to generate region proposal and produces region start mark and end mark and region expression class;All video frames are extracted to multiple scale features including local eccentricity features and global features;Weakly supervised classification is carried out to multiple scale features;The operation result of video stream level feature and picture level feature is classified by neural network, and depression recognition model is obtained.The present application effectively utilizes video label, takes into account global and local information, and combines the advantages of high accuracy and reduced label workload, can become a potential depression recognition auxiliary means, and can be widely applied in the field of automatic processing of mental illness.

The application provides a cross-domain small sample wideband signal detection and recognition method based on a visual basic model, and relates to the cross technical field of electromagnetic signal processing and computer vision. The application reduces the interference of background noise in the wideband time-frequency graph on the candidate area by performing double condition screening based on target confidence and aspect ratio features on the preliminary candidate frame, and combining the field of view expansion in the frequency axis direction, and improves the incomplete coverage problem of the slender signal candidate frame. By extracting multiple intermediate layer features and the final layer feature map of the visual basic model for fusion in the channel dimension, the problem that the local texture information in the wideband time-frequency graph is smoothed or covered in the layer-by-layer abstraction process is solved. The parameters of the visual segmentation model and the visual basic model are kept frozen, and only the small sample fine tuning of the front region proposal network is performed, so that the calculation overhead caused by full fine tuning of the visual basic model during cross-domain small sample adaptation is avoided, and the method is suitable for deployment on devices with limited computing resources.

This invention discloses a controlled knowledge transfer-based generalized small-sample target detection method and system for marine remote sensing. The method includes the following steps: extracting multi-scale features from remote sensing images to generate multi-scale feature representations; generating candidate region features from the multi-scale feature representations via a region proposal network; inputting the candidate region features into a parameter-independent base class feature extraction branch and a new class feature extraction branch to generate base class features and new class features, respectively; performing a two-level fusion process on the base class features and new class features to generate fused features; applying dual constraints to the fused features using knowledge distillation loss and mutual information regularization loss, whereby the knowledge distillation loss constrains the consistency between the fused features and the base class features, and the mutual information regularization loss reduces the redundant correlation between the new class features and the base features; and inputting the fused features into a classification branch and a regression branch to obtain the detection results.

The present disclosure provides a juvenile fish limb identification method based on a multi-scale cascaded perceptual convolutional neural network. The method includes the following steps: acquiring a video sequence of a juvenile fish, dividing a fish body into five non-overlapping parts, performing semantic annotation on the five non-overlapping parts, and taking the five non-overlapping parts as an input of the multi-scale cascaded perceptual convolutional neural network; and using a convolutional layer as a feature extractor, performing feature extraction on an input image containing the annotation of each limb, inputting extracted features into an Attention-region proposal network (RPN) structure, determining a category of each pixel, and generating a limb mask of each limb category using a multi-scale cascade method. According to the method, the limbs of the juvenile fish can be efficiently and accurately identified, and technical support is provided for posture quantification of the juvenile fish.

The present application relates to the technical field of detection data processing, and particularly relates to a textile fiber component analysis system and method based on machine learning; the direction gradient attention module provided in the embodiment has strong direction bias in feature representation before generating a candidate region proposal, which makes the region proposal network more easily locate the geometric center of the fiber according to the extension direction of the fiber, so as to generate a more compact and accurate candidate box, and lay a foundation for subsequent disentanglement of the adhered fiber. By focusing attention on several main directions related to the fiber, the direction gradient attention module naturally suppresses the edge response from random direction background noise, purifies the features, and improves the robustness of the model.

The invention relates to an open vocabulary target detection method based on a region proposal network and region correction, and the method comprises the steps: carrying out the feature extraction of a target image, and obtaining an initial feature map; inputting the initial feature map into a pre-constructed region proposal network to obtain a potential target region; based on the potential target region, guiding the initial feature map to perform region feature extraction; carrying out attention pooling processing on the extracted region features to obtain region embedding; performing text coding processing on categories in the detection list to obtain text embedding; and calculating the cosine similarity between the region embedding and the text embedding, and taking the category corresponding to the text embedding with the maximum cosine similarity with the region embedding as the category of the corresponding region. According to the invention, the accuracy and robustness of a target detection system in processing new types of targets can be improved, so that the high requirements of application scenes such as intelligent monitoring and automatic driving on target detection are met.

The invention discloses a 3D target detection method based on point cloud context structure information. The method comprises the following steps: collecting point cloud by using a data collector; voxelizing the point cloud to construct a 3D voxel representation; sending the 3D voxel representation into a backbone network to extract a multi-scale 3D voxel feature and a 2D BEV feature of the point cloud; sending the 2D BEV features into a regional proposal network to generate an initial proposal box; performing graded refining on the initial proposal box and the multi-scale 3D voxel features to generate an updated proposal box; and outputting a detection result according to the updated proposal box. According to the 3D target detection method, multi-scale point cloud context structure information can be effectively combined, the initial proposal box is iteratively optimized, and the detection performance is remarkably improved.

The invention discloses a target instance part segmentation method for part-level image segmentation, and the method comprises the steps: carrying out the preprocessing of an input image, carrying out the foreground target detection through a Faster R-CNN network, and obtaining candidate region features generated by a region proposal network; and then, constructing a target part segmentation branch to obtain a part segmentation mask, constraining the part segmentation mask by adopting part perception segmentation loss, and improving the segmentation precision of different parts of the target by combining the part perception segmentation loss with a plurality of pixel-level loss functions. And finally, fully training the target detection branch by using a progressive training strategy, and then training the target detection branch and the target part segmentation branch at the same time, thereby improving the network detection and segmentation quality. According to the invention, the problem that the existing image segmentation method is insufficient in target part recognition is solved.

The application discloses a kind of feature fusion-based remote sensing image small target detection method and system, and the specific steps of the method are as follows: the remote sensing image to be detected is input into the network architecture mainly constituted by convolutional neural network and feature fusion network;Image will enter convolutional neural network and region proposal network after entering network architecture, region proposal network will cooperate with convolutional neural network to carry out pooling operation on image region of interest, while the deep feature of image region of interest is output to feature fusion network, image will obtain image region of interest after entering region proposal network, and the manual feature of image region of interest is output to feature fusion network by calculation;Deep feature and manual feature enter feature fusion network, and feature fusion network includes two feature conversion modules and a feature embedding module.