212 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 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 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 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 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 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 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 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.

The invention relates to the technical field of image processing and computer vision, in particular to a general salient target detection method based on Mama, which is characterized in that a salient guide Mama module is adopted in a decoder. The module comprises the following steps: acquiring a rough saliency map, after initializing scanning parameters, traversing image lines according to the current direction, and recording a saliency block index; the scanning direction is dynamically adjusted based on the proximity relation between the current saliency block and the next line, and it is ensured that space continuity is kept during feature serialization. The method comprises the following steps: acquiring multi-modal input such as RGB; extracting a plurality of modal features through a twinborn visual state space encoder; carrying out feature fusion through a multi-mode converter; and finally, inputting the RGB hierarchical features and the fusion features into a decoder containing a saliency guide Mamba module to generate a high-precision saliency prediction map. According to the method, the problem of insufficient global dependency relationship capture in traditional scanning is effectively solved, and the detection precision is improved while the calculation efficiency is kept.

The invention discloses a multi-modal image saliency target detection method, which comprises the following steps of: constructing a training set comprising a color visible light image, an infrared image and a depth image, and constructing a neural network which consists of a feature extraction module, a three-modal feature fusion module and a combined decoding module, the feature extraction module extracts features and scale information of three modal images, the three-modal feature fusion module integrates features through a plurality of three-modal fusion modules, and the combined decoding module outputs a saliency target image through a plurality of prediction branches; a neural network is trained based on the training set to obtain a neural network model, and the neural network model can be used for testing saliency target detection of the image pair; the method has the advantages that the saliency target detection problem of the three-mode combined input image can be effectively solved, and the saliency target detection precision is high.

A method is disclosed to automatically detect overhangs from images with depth taken around an object during a scan of the object. An overhang detector can use an intersection of three filters based on these images and depth data associated with the images. The first filter looks for negative depth gradients along a 2D projection of a gravity vector, which is generally a vertical axis for images taken using a portrait orientation. The second filter selects the depth gradients that are oriented towards the projection of the gravity vector. The third filter is a salient object detection mask computed from the image. An intersection of the three filters can then be used to obtain overhangs. The method can be implemented in real time with a User Interface (UI) directing a user of a location of the overhang so that an image below the overhang can be taken.

The invention provides an RGB-D lightweight semantic segmentation method fusing frequency domain guidance, relates to the technical field of image processing, and designs a frequency domain guidance prompt adapter to improve consistency and propagation efficiency of cross-layer semantic features. Secondly, a spectrum-guided dynamic convolution module is provided, and efficient multi-scale feature modeling is realized while spatial domain and frequency domain features are fused. And finally, constructing a multi-scale frequency domain agency attention module, and enhancing semantic interaction and global modeling capability among different scale features in a low-overhead mode. According to the method, on a plurality of RGB-D and RGB-L semantic segmentation data sets, excellent segmentation performance can be achieved with low parameter quantity, good generalization ability is shown in five data sets in an RGB-D salient target detection task, and research results show that the segmentation result of the method is more accurate in a scene with a complex structure.

The present invention belongs to the technical field of computer vision and image processing, and in particular relates to an interactive saliency mining method for RGB-D salient object detection, comprising the following steps: S1, using a two-stream encoder network to extract multi-level cross-modal features of RGB and depth images; S2, proposing a cross-modal interaction module to achieve the interaction and aggregation of these different features through a series of matrix operations; S3, attempting to separate the saliency perception information from the complex environment and establishing a fusion method between adjacent features under the guidance of a relatively rough saliency map; S4, further extracting context information from the saliency perception information and background information. The present invention obtains multi-layer cross-modal fusion features in the encoding stage, uses a progressive saliency mining module to decode multi-level features, gradually filters out complex background interference to make the saliency map more accurate, gradually refines the saliency region to make the saliency map more precise, and realizes the optimal decoding process.

The present invention aims to provide a salient target detection method for RGB-D images, comprising: sampling RGB image and depth map samples at a specified number to form sampling data, and preprocessing the sampling data using data augmentation techniques to obtain data to be processed; in the cross-modal fusion of each layer in the feature extraction stage, using an attention mechanism to infer salient regions to determine the saliency degree of different regions, and fusing at each layer to obtain low-level features and high-level features; performing concatenation and convolution operations on the high-level features and the low-level features to obtain joint features of RGB features and depth features; dynamically allocating the weights of RGB image and depth map features according to the joint features of RGB features and depth features to obtain a salient target detection model; and realizing the salient target detection result according to the salient target detection model. The method described in this invention can detect salient targets well and handle scenes with low depth map contrast.

This invention relates to a weakly supervised video salient object detection method and system based on memory-edge guidance, belonging to the field of object detection technology. It includes: dividing a given long video sequence into non-overlapping sliding windows to extract several consecutive video frames; inputting these video frames into a trained memory-edge guidance network model to achieve weakly supervised video salient object detection; specifically, it includes: extracting features from consecutive video frames at the current time step to obtain spatiotemporal features extracted at different scales; using salient cues mined from historical frames to enhance the semantic representation of relevant objects in the current frame; and then inputting these features into a decoder to obtain the final salient object detection map. This invention achieves accurate localization and fine segmentation of salient objects in videos.

The present application provides an image processing method, device and equipment, which can be applied to the technical field of image processing. The image processing method comprises: pre-processing an input image to obtain input features; inputting the input features into a visual encoder and a multi-layer perception machine in a visual center decoupler respectively to obtain enhanced special features and salient object detection special features; the visual encoder aggregates local region features based on the input features to obtain the enhanced special features, and the multi-layer perception machine captures edge information based on the input features to obtain the salient object detection special features; inputting the enhanced special features into an enhancement network to obtain enhanced output features; the enhancement network takes illumination weights of different color channels and local binary pattern features of the input image as illumination constraints, and enhances the enhanced special features to obtain the enhanced output features; and inputting the salient object detection special features and the enhanced output features into a salient object detection network to detect a salient object.

The present invention relates to an infrared video salient object detection method based on deep learning and differentiable clustering, comprising: 1. acquiring infrared video image frames and constructing an infrared video saliency dataset; 2. constructing an infrared video salient object detection model, which mainly consists of a feature extraction network based on the Vgg16 network, a saliency detection model based on attention and ConvLSTM, and a salient object fine segmentation model based on differentiable clustering. The input image is subjected to feature extraction by the feature extraction network, and the extracted features are then input into the saliency detection model to obtain a dynamic saliency map. Finally, the salient object fine segmentation model is used for refined segmentation to obtain the image segmentation result. The infrared video salient object detection model is trained with the dataset; 3. inputting the image to be detected into the trained model to obtain the detection result. This method can improve the accuracy of infrared video saliency detection and clearly and accurately detect the salient regions in infrared video objects.

The present application relates to a kind of full supervision's salient object detection method, constructs complete multi-branch feature fusion refinement network MFFRNet as salient object detection model;Again training set in data set is input to the proposed MFFRNet model training, every time completing a round will be back propagated once, to optimize MFFRNet model parameter;With data set test set, the performance of model is evaluated;Finally, the model after evaluation is used for salient object detection.The model effectively fuses the detail information of low-level feature and the semantic information of high-level feature.The module designed for low-level feature utilizes asymmetric convolution to reduce background noise and other interference factors, and a module designed for high-level feature obtains rich semantic information.Meanwhile, aliasing effects caused by frequent up-sampling are effectively handled.The method effectively captures salient objects and obtains saliency prediction map, and has strong robustness.