249 results about "Salient object detection" patented technology

The invention discloses an optical remote sensing image salient target detection method based on progressive attention enhancement, and belongs to the technical field of computer vision. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a hierarchical progressive fusion encoder, capturing a global irregular topological structure and local fine-grained image details, and realizing cross-hierarchical feature fusion; inputting the output characteristics of the encoder into a global context enhancement module, and capturing multi-level context information by adopting a parallel multi-branch structure; and inputting the output features of the hierarchical progressive fusion encoder and the global context enhancement module into a multi-scale progressive attention enhancement decoder, carrying out hierarchical decoding on the input features by adopting a saliency-guided attention mechanism, and gradually aggregating deep semantic information and shallow detail features to realize coarse-to-fine progressive optimization, so as to improve the robustness of the multi-scale progressive attention enhancement decoder. And finally generating a saliency map. The method can effectively improve the processing performance of an irregular topological structure and a complex context relationship in the optical remote sensing image.

The invention discloses an SAM2 multi-task perception binary segmentation method based on a hybrid expert adapter. The method is specifically implemented according to the following steps: step 1, constructing a data set and an encoder; step 2, constructing a hybrid expert adapter module; and step 3, constructing a task awareness gating module. According to the method, a pre-trained SAM2 is taken as a main network, on the basis of freezing the main body weight, a standard adapter and a MoE-Adapter are respectively deployed on odd and even layers of an encoder, and a lightweight expert sub-network and a dynamic gating strategy are combined, so that unified processing of multiple tasks such as salient target detection, camouflage target identification, marine animal segmentation and the like is realized.

The invention discloses an optical remote sensing image salient target detection method based on double-branch progressive interaction and multi-direction feature enhancement. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a double-branch progressive interaction encoder for feature extraction, inserting a large windmill into a CNN branch to capture complex shape features, and realizing cross-branch interaction and weight adjustment by using a progressive interaction mechanism; inputting the extracted features into a semantic association enhancement module, generating a semantic association weight through a context awareness attention mechanism, and enhancing the semantic consistency of the significant target; inputting the enhanced features into a multi-scale adaptive fusion module, dynamically adjusting a feature fusion proportion, and introducing a deformable pooling operation to avoid loss of key features; and finally, inputting the fusion features into a decoder, recovering the image resolution through cross-layer connection, and generating a salient target detection result. According to the method, the problems of insufficient feature fusion and poor multi-scale target adaptability are effectively solved.

The invention provides an RGB-D salient target detection method and system based on cross-modal correlation learning, and the method comprises the steps: constructing a feature extraction network of an RGB mode and a depth mode based on a double-flow encoder architecture; performing feature fusion by using an adaptive cross-modal fusion module to obtain cross-modal fusion features; performing attention enhancement on the refined large-scale features and the small-scale up-sampling features by utilizing element-by-element multiplication to obtain output fusion features of a multi-scale feature aggregation module; performing object boundary prediction on output fusion features of the multi-scale feature aggregation module by using an up-sampling layer and a convolution layer to obtain features of an edge prediction map; performing point-by-point addition operation on the saliency features to obtain a final saliency prediction map; inputting the RGB-D data into a network model for training so as to generate an RGB-D salient target detection result; according to the method, the relevance and complementarity between the modals are mined, the characteristic information of different modals is fully integrated, and the accuracy and robustness of salient target detection are improved.

The invention belongs to the technical field of saliency target detection, and particularly relates to a saliency target detection method based on edge perception and an attention mechanism. The invention provides a saliency target detection method based on edge perception and a dual-channel attention mechanism, and aims to solve the problems that most of conventional saliency target detection methods are low-resolution image processing, high-resolution image samples are scarce, accurate detection is difficult and the like. By introducing an edge sensing module, target boundary information is accurately extracted, and the structural features of an image are enhanced, so that the target detection precision under low contrast and complex backgrounds is effectively improved; through a double-channel attention mechanism, features are weighted in a spatial domain and a channel domain, a salient region is adaptively focused, background interference is suppressed, and the robustness and accuracy of a detection model are improved. The method can effectively adapt to a saliency target detection task in a complex scene, and meets the actual engineering requirements.

The invention discloses a three-mode saliency target detection method based on feature fusion enhancement, and the method comprises the steps: firstly carrying out the preprocessing of a training set and a test set in a three-mode saliency target detection data set; secondly, constructing a three-mode saliency target detection network based on feature fusion enhancement; and finally, inputting the preprocessed training set image into a three-mode saliency target detection network, and generating a prediction map with the same size as the input image. And carrying out loss calculation by using a loss function, carrying out optimization through back propagation, and carrying out testing through the preprocessed test set image. According to the method, the precision of multi-modal saliency target detection is improved, and the complementarity and global perception capability among multi-modal information are enhanced.

The invention relates to a memory-edge guidance-based weak supervision video saliency target detection method and system, and belongs to the technical field of target detection. Comprising the following steps: performing non-overlapping sliding window division on a given long video sequence, extracting a plurality of continuous video frames, and inputting the plurality of video frames into a trained memory edge guide network model to realize weak supervision video saliency target detection; inputting to a trained memory edge guidance network model to realize weak supervision video saliency target detection; the method specifically comprises the following steps: performing feature extraction on continuous video frames of a current time step to obtain spatio-temporal features extracted from different scales; saliency clues mined from historical frames are utilized to enhance semantic representation of related objects in a current frame, then the semantic representation is input into a decoder to be decoded, and a final saliency target detection map is obtained. According to the invention, accurate positioning and fine segmentation of the saliency target in the video are realized.

The invention discloses a saliency target detection method and system based on three-mode feature depth interactive fusion, and relates to the technical field of image segmentation. According to the invention, hierarchical feature extraction of visible light, depth and thermal imaging multi-modal input is realized by constructing a parallelized three-stream encoder network architecture, and a three-modal information interaction fusion module is introduced at the encoder hierarchy, and an adaptive weight distribution mechanism is designed, so that the multi-modal information interaction fusion algorithm is realized. Deep interaction and complementary information mining of cross-modal features are realized in space and channel dimensions, a three-modal information interaction fusion module comprises an inter-modal attention mechanism and a feature enhancement unit, and contribution degrees of different modal features can be dynamically adjusted; a dynamic feature enhancement module is designed for a visible light mode, and selective enhancement of salient region features is realized through depth separable convolution of an integrated pyramid structure.

The invention provides a saliency target detection method and system based on edge detection and an attention mechanism, relates to the technical field of computer vision, and utilizes a target detection network to carry out saliency target detection. The target detection network emphasizes the selectivity and invariance of the features during detection of a salient edge and a salient region through parallel edge branches and salient detection branches; the edge branch performs interactive fusion on the low-level features with spatial structure details and the high-level features with rich semantic knowledge to obtain edge features; the saliency detection branch generates a region feature containing multi-scale key information through multi-scale attention, and improves the interior and boundary of the region feature through an edge guide learning strategy by using edge features as guidance to obtain a final saliency target region; according to the method, edge detection and an attention mechanism are combined, saliency target identification and positioning and image edge definition are better realized, and the accuracy of saliency target identification of the system is improved.

The invention relates to the field of saliency target detection, and particularly discloses a multi-dimensional frequency domain and deformable attention fusion saliency target detection method, which comprises the steps of S1, inputting an infrared image to be detected; s2, performing multi-scale feature extraction and fusion to obtain low-level and high-level features; s3, phase spectrum analysis is carried out to extract frequency domain primary perception features; s4, fusing the frequency domain features to obtain frequency domain saliency features; s5, a deformable space attention module extracts space enhanced perception features; and S6, fusing the features to generate a prediction map and constraining the prediction map by a loss function. According to the method, the problems of insufficient frequency domain utilization, weak global context and detail retention and poor complex deformation target detection of an existing spatial domain method are solved, and the detection precision and robustness of a multi-scale and deformation target in a complex scene are effectively improved.

The invention relates to the technical field of computer vision, in particular to an RGB-D salient target detection method based on semantic features and biological inspiration. The method comprises the following steps: performing feature extraction on an RGB-D image sample through an encoder to obtain low-level, middle-level and high-level semantic features of an RGB image and a depth image; fusing the low-level semantic features of the RGB image and the depth image through a multi-stage fusion module to obtain low-level fusion features; fusing the intermediate semantic features of the RGB image and the depth image to obtain intermediate fusion features; fusing the advanced semantic features of the RGB image and the depth image to obtain an advanced fusion feature; and decoding the low-level, middle-level and high-level fusion features through a cortex decoder to obtain a saliency prediction map. According to the method, the efficiency, the robustness and the generalization ability of the RGB-D saliency target detection model are improved.

The invention relates to a lightweight double-flow cross-modal interaction RGB-D saliency target detection method, which comprises the following steps: S1, data preparation: obtaining an RGB-D data set of a task for training and testing, taking a part of an NJU2K data set and a part of an NLPR data set as a training set, and taking the training set as a training set; taking the rest of the NJU2K data set, the rest of the NLPR data set, the rest of the SIP data set, the rest of the STERE data set and the rest of the SSD data set as test sets; s2, constructing a network model: S21, constructing a feature extraction backbone network of a decoder, S22, constructing a self-adaptive cross-modal fusion module (ACM), and S23, constructing a multi-scale cavity attention module (MSA); the method comprises the following steps: S24, setting a decoder, S25, calculating a loss function, and S26, evaluating indexes; the method has the advantages that high calculation efficiency can still be kept under the condition of low model complexity, and meanwhile high generalization and accuracy are achieved when multiple types of scenes are processed.

The invention discloses a style migration method for separating content image objects, which comprises the following steps of: extracting a style image and a content image of a target image, processing the style image by using a salient target detection module, and distinguishing a style main object from a background object; constructing a depth estimation mechanism, processing the content image through a depth estimation module, and obtaining depth information of the content image; on the basis of the style separation attribute of the convolutional feature channel, extracting the features of the style subject object, and designing an opacity sensitive loss function to obtain a stylized subject object migration result; in combination with the characteristic that the content background contains a complex object, based on defocusing and instance normalization, the definition and style information of the content background are adjusted to obtain a stylized background migration result; and finally, combining object migration results. When facing a content image with a disordered content main body, the method guides the style migration module to realize style migration through a depth estimation mechanism, and effectively solves the problem of disordered style migration results of a complex scene.

The invention relates to the technical field of light field image salient target detection, in particular to a light field salient target detection method based on edge perception and hierarchical fusion, and the method comprises the steps: carrying out the multi-scale feature extraction of a focus stack image and a full-focus image through a backbone network; performing edge fusion enhancement on the focus stack features of four layers of different scales through an SEPM module and an EFM module; fusing high-level multi-modal semantic information from the global and local aspects by using an LHFM module; fusing low-layer space information and refining a salient target by using an LLFM module; and aggregating multi-scale information of a high layer and a low layer, and decoding the multi-scale information into an accurate saliency prediction image by using a detection head. According to the method, edge perception and a lightweight hierarchical fusion strategy are combined, the model parameter quantity and the calculation complexity are remarkably reduced while the high detection performance is kept, and the optimal balance between the performance and the efficiency is achieved.

The invention provides a mask prior-based two-stage infrared and visible light image fusion method, belongs to the technical field of infrared and visible light image fusion, and solves the problem that scene information cannot be comprehensively expressed by an infrared or visible light single image. The method comprises the following steps: 1, preprocessing infrared and visible light source images in an image database, and dividing the infrared and visible light source images into a training set and a test set; 2, training a salient target detection network to obtain a trained salient target detection network model; and 3, inputting the infrared source image into the salient target detection network to obtain a mask image. And 4, constructing an image fusion network, and training the image fusion network to obtain a trained image fusion network model. And 5, testing the images in the test set by using the image fusion network model to obtain a fused image. According to the invention, based on the mask priori knowledge, the saliency information in the infrared image can be effectively extracted, so that the obtained fusion image can better highlight the target information, and the influence of environments such as strong light on the fusion image is weakened.

The invention discloses a three-mode saliency target detection method and system based on frequency domain decomposition and reconstruction. The method comprises the following steps: firstly, respectively preprocessing a training set and a test set in a three-mode saliency target detection data set; secondly, constructing a three-mode saliency target detection network based on frequency domain decomposition and reconstruction; and finally, sending the preprocessed training set image into a three-mode saliency target detection network for processing, outputting a prediction map consistent with the input image in size, completing target detection, and performing training and testing. According to the invention, through designing the interaction, fusion and enhancement network, the information complementation advantages of three modes of visible light, depth and thermal imaging are fully utilized, the synergistic interaction and global perception efficiency among multi-mode information are further enhanced, and accurate salient target detection is realized.

The invention discloses a weak supervision semantic segmentation method and device, electronic equipment and a medium, and the method comprises the steps: carrying out the salient target detection of an input image through a salient target detection module, so as to capture the complete contour and edge details of a salient target in the input image; generating a class activation graph by using the classification network to estimate the position of the target object in the image; a salient target selector module is designed, and a salient target detection result and the class activation graph are fused to generate a pseudo tag; carrying out weak supervision training on the semantic segmentation model by utilizing the generated pseudo tag so as to improve the overall performance of the semantic segmentation model; and obtaining a to-be-processed image, inputting the image into the trained semantic segmentation model, and outputting the segmented image. According to the method, the high-quality pseudo tag is generated through complementary fusion of the salient target detection and the class activation graph, the semantic segmentation precision is remarkably improved, the segmentation precision of the model in a complex scene is improved, and the method can be widely applied to the field of image processing.

The invention discloses an RGB-T salient target detection method based on asymmetric illumination perception progressive decoding. The method comprises the following steps: acquiring an RGB-T salient target detection data set, and inputting the RGB-T salient target detection data set into a trained RGB-T salient target detection network; the method comprises the following steps of: acquiring a multi-level feature by adopting two Swin Transform backbone networks; effective interaction of RGB features and T features is realized in an asymmetric mode by using an asymmetric illumination perception interaction module, and interference information is reduced; through a channel spatial feature fusion module, cross-modal feature fusion is realized in two dimensions of a channel and a space. Decoding the fused features by adopting a staged progressive decoding strategy, and outputting a final saliency map; according to the method, a cross-modal feature interaction and fusion method and a multi-scale information aggregation strategy are explored, and the precision of RGB-T salient target detection is improved.

The invention discloses an RGB-D salient target detection method based on decoupling contrast learning, and designs a saliency detection framework integrating expression enhancement, modal collaborative perception and structural discrimination learning by combining a structural heterogeneity problem in multi-modal modeling and utilizing the frequency domain structural advantage of a deep mode and the long-distance modeling capability of Transform. By introducing wavelet convolution and Transform joint modeling, a cross-modal interaction parallel fusion mechanism and a pixel-level structure perception contrast learning strategy, high-precision, multi-scale and boundary clear detection of a salient target area in a complex scene is realized. The method can effectively solve the problems of large information difference between modes of the RGB and the depth map, difficulty in structure alignment, fuzzy boundary prediction, weak feature expression ability and the like, significantly improves semantic consistency and structural integrity of the salient region, and has good cross-modal generalization ability and robustness.

The invention discloses an RGB-D saliency target detection method, and relates to the technical field of computer vision. Comprising the following steps: acquiring a color image, depth information and a corresponding RGB-D saliency target annotation graph from an RGB-D saliency detection data set; inputting the color image and the depth information into a cross-modal saliency detection network to obtain an RGB-D saliency target prediction map; the cross-modal saliency detection network is trained through the RGB-D saliency target prediction map and the RGB-D saliency target annotation map, and the trained cross-modal saliency detection network is obtained; and inputting a to-be-processed color image and to-be-processed depth information into the trained cross-modal saliency detection network to obtain an RGB-D saliency target recognition graph. According to the method, the visual integrity and detail fidelity of saliency detection are remarkably improved, and the accuracy of a saliency detection result is enhanced.

The invention discloses a saliency target detection method based on a Mama network two-way guidance model. The method comprises the steps that a two-way model framework based on the Mama network is composed of an encoder branch, an edge branch, a saliency branch and a decoder branch; the encoder branch performs block division on the obtained original image to be detected, inputs the image to the Mama feature extractor and performs down-sampling to obtain five-layer features, the first three-layer low-layer features are respectively subjected to convolution processing and then are used as edge features to be input into the edge branch, the first four-layer features are used as significant features to be input into the significant branch, and the significant branch is used as edge features to be input into the edge branch; the fifth-layer high-level features are subjected to two-stage mixed attention processing, global features are obtained, and positioning guidance is provided for subsequent feature fusion; the edge branch performs receptive field expansion and spatial enhancement on the input edge features to obtain edge fusion features. According to the invention, the precision and efficiency of saliency target detection can be well improved.

The invention discloses a panoramic image saliency target detection method, system and device based on depth information fusion and a medium, belongs to the field of computer vision, and is suitable for a panoramic image analysis scene of high-precision target detection. The method comprises the following steps: performing depth estimation and saliency target detection on a panoramic image to obtain a depth image and a saliency target image, extracting features from the depth image and the saliency image respectively to obtain a depth image flow feature and a saliency image flow feature, fusing the depth image flow feature and the saliency image flow feature to generate a fusion feature, and decoding the fusion feature to obtain a depth image flow feature and a saliency image flow feature; obtaining a detection result; performing joint training on depth estimation, saliency target detection and feature fusion according to a detection result, and finally inputting a panoramic image into the trained network model, detecting a saliency target, and obtaining a saliency target result map; the system, the equipment and the medium are used for implementing the method. According to the method, noise and redundant information are suppressed, and the accuracy of saliency target detection of the panoramic image is improved.

The invention provides a high-precision integrated imaging 3D salient target detection method with texture features, and the method comprises the steps: obtaining four-dimensional light field data, carrying out the high-precision salient target detection of a micro-image array, carrying out the texture feature compensation of a salient micro-image array, and carrying out the three-dimensional reconstruction of a high-precision salient micro-image array with texture features. According to the method, collected four-dimensional light field data is used for generating a micro-image array, the micro-image array is used as input of high-precision salient target detection, cross-regional global salient features are fully mined through recombination folding operation and cyclic coding and decoding, a high-precision salient micro-image array is generated, and then a layered texture feature compensation method is used for realizing high-precision salient target detection. According to the method, the texture features of the region salient micro-image array and the edge salient micro-image array are respectively restored and generated, and finally three-dimensional reconstruction is performed on the high-precision salient micro-image array with the texture features by means of the lens array, so that a subsequent computer vision task is effectively simplified, and fine and colorful three-dimensional display is provided for a salient target.

The invention belongs to the field of salient target detection, and provides an underwater image salient target detection method and system based on a conditional diffusion model, and the method comprises the steps: carrying out the feature extraction of an RGB image and a depth image in each time step, and obtaining a plurality of RGB features and depth features with different resolutions; fourier domain perception enhancement is carried out on the RGB features and the depth features under the same resolution, and global Fourier optimization features are obtained; generating splicing features based on the RGB features and the depth features under the same resolution, and performing spatial domain perception enhancement on the splicing features to obtain spatial optimization features; fusing the global Fourier optimization features and the spatial optimization features to obtain fusion results, and fusing the fusion results under different resolutions to obtain condition features of each time step; and predicting the condition features of different time steps and the loading reference image to obtain prediction results of different time steps, and screening and aggregating based on the prediction results of different time steps to obtain an aggregation detection result.

In accordance with techniques for controlling hallucinations in generated images, a generative image model receives an input image depicting an object and having a first background, and the generative image model produces a generated image depicting the object by replacing the first background with a second background. Further, a salient object detection model generates a first object mask and a second object mask. The first object mask defines a first positioning of the object within the input image, while the second object mask defines a second positioning of the object within the generated image. A hallucination metric capturing an amount of deformation introduced into the object by the generative image model is determined based on a comparison of the first object mask and the second object mask. In one or more implementations, the generated image is output based on the hallucination metric meeting a threshold.

The present invention proposes an RGB-D salient target detection method and system based on cross-modal association learning. The method comprises: constructing a feature extraction network of RGB modality and deep modality based on a dual-stream encoder architecture; using an adaptive cross-modal fusion module to perform feature fusion to obtain cross-modal fusion features; using element-by-element multiplication to perform attention enhancement on refined large-scale features and small-scale up-sampled features to obtain output fusion features of a multi-scale feature aggregation module; using an upsampling layer and a convolutional layer to predict the object boundary of the output fusion features of the multi-scale feature aggregation module to obtain the features of an edge prediction map; performing point-by-point addition operations on salient features to obtain a final salient prediction map; inputting RGB-D data into a network model for training to generate RGB-D salient target detection results; the present invention improves the accuracy and robustness of salient target detection by mining the correlation and complementarity between modalities and fully integrating the characteristic information of different modalities.

The invention relates to the technical field of artificial intelligence, and discloses a target detection method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed vehicle image, inputting the to-be-processed vehicle image into a block embedding layer of an SAM model image encoder, and generating block embedding features; acquiring data set sample information, inputting the data set sample information into an adapter of the SAM image encoder, and generating prompt features; inputting the block embedding features and the prompt features into an encoding layer of an SAM image encoder to generate an encoding vector; and inputting the coding vector into an SAM model image decoder to generate a salient target detection result corresponding to the vehicle image. The adapter model can learn data sample information and transmit the data sample information to the SAM image encoder as a prompt, adaptive training is efficiently performed in a salient target detection task through additional training of a small amount of data, the salient target detection accuracy is improved, and the segmentation efficiency of the vehicle image is further improved.

The invention discloses an underwater salient target detection method and system based on a double-flow fusion network, and belongs to the technical field of computer vision. The method comprises the following steps: respectively extracting multi-scale features of an RGB image and a depth image through a double-flow encoder; in the shallow layer, fusing and enhancing the edge and detail information of the bimodal features through an edge fusion module; in a deep layer, content-adaptive cross-modal semantic fusion is realized in a frequency domain through a dynamic filtering module; fusing the multi-scale features through a cross-layer aggregation decoder to generate a rough saliency map; extracting detail features from the original RGB image through a global detail purification network; and finally, fusing the rough saliency map and the detail features, and outputting an underwater saliency target prediction map. The objective of the invention is to improve the precision and boundary definition of salient target detection in an underwater complex scene.

The invention discloses an optical remote sensing image salient target detection method based on Mama dynamic clustering and bidirectional calibration, and belongs to the field of computer vision. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a lightweight encoder, capturing multi-scale features and refining local textures and edges; the multi-scale features output by the lightweight encoder are input into a dynamic clustering module based on Mamba, and interaction enhancement of global semantic modeling and dynamic local feature capture is achieved; inputting the output features of the Mama-based dynamic clustering module into a bidirectional cross-scale calibration module to realize cross-scale feature bidirectional complementation and semantic detail enhancement; inputting the output features of the bidirectional cross-scale calibration module into an edge attention combined repair module to realize attention hole repair and boundary precision enhancement; and finally, realizing feature aggregation and spatial resolution recovery through a decoder, and finally generating a saliency map. The method is used for solving the problems of target scale inconsistency and boundary blur in the remote sensing image.

The invention provides a multi-domain and Mama collaborative saliency target detection method oriented to a 360-degree image, mainly relates to a saliency region detection method oriented to image equatorial region structure modeling and global guidance enhancement, introduces PVT as a backbone network, extracts multi-scale features, inputs the multi-scale features to a frequency domain-space domain coordination module, and finally, obtains a multi-scale target detection result. The multi-scale features extracted by the PVT backbone are fully fused through frequency domain and spatial domain information, so that multi-scale edge details in the image can be effectively captured, and the significance boundary of the equatorial region is enhanced; an attention fusion Mama module is introduced, by fusing output features of a frequency domain-space domain coordination module, the Mama module can effectively improve structural guidance and semantic complementation of equator saliency information on a polar region, and finally a lightweight multi-stage feature aggregation module is designed for generating a saliency feature map. According to the detection method provided by the invention, the most advanced performance can be obtained under the condition of relatively low calculation complexity.