80 results about "Salient object detection" patented technology

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 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 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 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.

The application discloses an RGB-D salient object detection method based on boundary deformable convolution guidance, comprising the following steps: step one, respectively extracting features of an RGB mode and a depth map mode; step two, fusing the features of the two modes through a cross-modal attention fusion feature module to mine common and complementary features of salient objects; step three, inputting the feature map into an encoder deep layer embedded with an adjacent multi-scale feature enhancement module to obtain global context feature information; step four, generating a boundary clue map of the salient objects by constructing a boundary feature extraction module; and step five, generating a saliency map by using the generated boundary clue map and deformable convolution guidance. The application mines and strengthens the commonness of salient objects by cross-fusion of the depth map and the RGB image, effectively captures salient objects with different sizes and uncertain quantities by using adjacent level feature interaction, and solves the boundary blur problem of the saliency map by using the edge clue map to guide the model decoding.

The invention discloses an RGB-D-based salient target detection method and system, a storage medium and equipment, and relates to the field of target detection, and the method comprises the steps: obtaining a picture: obtaining a to-be-detected RGB picture and a to-be-detected depth picture; in the stage of fusion coding, the RGB picture and the depth picture are respectively input into an RGB channel and a depth channel of a ResNet-50 convolutional neural network as a backbone network for processing; on the basis of a space channel attention mechanism, performing feature fusion on output of corresponding layers of the RGB channel and the depth channel to obtain RGB fusion output and depth fusion output of a corresponding stage; multi-stage fusion coding, wherein RGB fusion outputs of different stages are fused to obtain RGB multi-stage feature fusion output; fusing the depth fusion outputs of different stages to obtain depth multi-stage feature fusion output; and decoding: merging, compressing and fusing the RGB multi-stage feature fusion output and the depth multi-stage feature fusion output to obtain a detection result of the salient target. The purpose of improving the detection precision is achieved.

The invention discloses a saliency object detection method, device and equipment, and belongs to the technical field of image processing. The saliency object detection method comprises the following steps: acquiring a first image; dividing the first image into a plurality of color areas according to the pixel values of the pixel points in the first image; wherein the pixel value similarity of the pixel points in the same color area is greater than or equal to a pixel value similarity threshold; extracting at least one feature of each color area; determining a contrast of the first color region relative to the first feature; wherein the first color region is any one color region in the plurality of color regions, and the first feature is any one feature in the at least one feature; determining a histogram contrast of the first color region; determining a first comprehensive contrast ratio of the first color area according to the contrast ratio of the first color area relative to the first feature and the histogram contrast ratio of the first color area; and determining a salient object in the first image according to the first comprehensive contrast of the plurality of color regions.

The application discloses a kind of based on progressive attention enhancement optical remote sensing image salient target detection method, belong to computer vision technical field.The method includes: the original data set is preprocessed;The image after pre-processing is input hierarchical progressive fusion encoder, captures global irregular topological structure and local fine-grained image details, and realizes cross-level feature fusion;The output feature of encoder is input global context enhancement module, adopts parallel multi-branch structure, captures multi-level context information;The output feature of hierarchical progressive fusion encoder and global context enhancement module is input multi-scale progressive attention enhancement decoder, adopts saliency guided attention mechanism, carries out hierarchical decoding to input feature, gradually aggregates deep semantic information and shallow detail features, realizes from coarse to fine progressive optimization, finally generates saliency map.The application can effectively improve the processing performance of irregular topological structure and complex context relationship in optical remote sensing image.

The application discloses a thumbnail generation method based on salient object detection and image quality evaluation, comprising the following steps: 1) preparing a salient object detection data set and training a model YOLO_SAL based on the data set; 2) inputting an image into the model YOLO_SAL to determine a salient core region of the image; 3) generating a to-be-screened set around the salient core region of the image through a cropping algorithm; and 4) screening out a thumbnail with the best aesthetic quality from the to-be-screened set through an image quality evaluation model SAMP_Net based on a composition rule. The application solves the problems of the existing thumbnail generation method based on deep learning, such as complex model, complicated steps, difficulty in landing and incomplete labeling target, etc. by combining YOLO_SAL and SAMP_Net, and improves the detection speed and the aesthetic quality of the thumbnail while ensuring that the predicted image core region has saliency and integrity.

The invention provides an RGB-D image salient target detection method based on frequency decoupling mode interaction, and the method comprises the steps: firstly carrying out the multi-stage feature extraction of RGB-D mode data, and obtaining the multi-stage feature representation of an RGB-D mode; performing frequency domain sensing cross-modal interaction on the RGB-D modal multi-stage feature representation to obtain frequency sensing cross-modal spatial domain interaction features; discriminative enhancement and cross-modal fusion are carried out on the cross-modal spatial domain interaction features of frequency sensing, and final multi-modal fusion features are obtained; and based on the final multi-modal fusion features, through multi-scale aggregation and global dependence modeling, generating a saliency target detection prediction map. According to the method, cross-modal association in a frequency domain is explicitly modeled in a feature learning process by using a frequency cross Mama fusion module, so that the problem that internal relationships among modals may be ignored when fusion is directly performed in a spatial domain in a traditional method is remarkably relieved, and the performance and generalization ability of salient target detection are improved.

The invention discloses a lightweight three-mode saliency target detection method based on Mama, 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 lightweight three-mode salient target detection network based on Mamba, inputting test set data, and obtaining a detected target detection result; the three-mode saliency target detection network comprises a lightweight encoder based on Mama, a 3DMama fusion layer, and a decoder module based on a depth separable convolution DWConv. And finally, constructing a joint loss function to train a three-mode saliency target detection network, and testing the three-mode saliency target detection network. According to the method, complementary information is effectively fused, the efficiency and performance of the model are remarkably improved, and target detection is accurately and efficiently completed.

This application provides a method for detecting salient objects in images. It constructs a detection model based on a lightweight Mobilenetv2 backbone network and introduces fusion side connections into this backbone network to progressively fuse features from each layer. The method predicts salient objects at multiple scales and performs supervised learning, effectively avoiding overfitting. The proposed method constructs a lightweight detection model, and by introducing fusion side connections, it fully integrates features from each layer, making the model's performance comparable to larger existing models. The lightweight and high-performance detection model constructed in this application is suitable for deployment on television terminals and can be applied to television application scenarios such as visual object tracking and intelligent picture quality settings.

The application discloses a kind of construction and detection method of lightweight salient object detection model based on multi-scale learning, first, the features of color three channels RGB image are extracted using multi-scale learning mechanism and depth separable convolution;Second, the features of depth image are extracted using reverse residual block;Third, the features of depth image are enhanced using channel attention mechanism and spatial attention mechanism;Then, the features of color three channels RGB image and enhanced depth image are fused;Finally, the fused features pass through decoding network and output layer to generate saliency prediction map.The model reduces the number of parameters through depth separable convolution, performs multi-scale learning through dilated convolution, and improves the effect of salient object detection by mining the effective information of depth image through attention mechanism.

The invention discloses a saliency target detection system and a saliency target detection method based on Swin transform, and aims to solve the problems of insufficient cross-modal feature fusion and inaccurate target boundary positioning in the existing method. The invention innovatively designs a cross-modal bidirectional fusion network (CENet), constructs a complete encoder-feature fusion-decoder framework, and focuses on cross-modal deep fusion and edge detail refinement. According to the framework, two key modules, namely a cross-modal bidirectional fusion module (CMBF), are designed, and bidirectional deep interaction and efficient complementation of RGB and Depth features are achieved through a symmetric cross attention mechanism. And the edge perception refining module (EAR) is used for deeply fusing low-level features containing accurate boundary information and high-level semantic features through a double-pooling mechanism and coordinate attention and then introducing the fused features into a middle layer of a decoder, so that the target edge positioning capability is remarkably improved.

The application discloses an attention edge interaction optical remote sensing image saliency target detection method, aiming at improving the detection accuracy of saliency targets in optical remote sensing images. The current saliency target detection of optical remote sensing images has the following two problems: due to the insufficient utilization of edge information, the saliency target prediction map is prone to boundary blur in some complex scenes of optical remote sensing images; in the process of gradually transferring the high-level semantic information extracted by the model to the shallow layer, the position information captured by the deeper layer may be gradually diluted at the same time. In view of the first problem, a multi-scale attention interaction module is designed to effectively fuse fine edge features. In view of the second problem, a semantic guidance fusion module is designed to reduce the information loss of low-level features in the fusion process. In combination with the above two designs, the model designed by the application can robustly and accurately detect the saliency targets in the optical remote sensing images, and has the ability to process various complex scenes, and is worth popularizing.

The invention discloses an RGB-D video feature fusion and saliency target detection method based on modal guidance enhancement. The fusion method comprises the following steps: acquiring an RGB image and an optical flow image of an object; extracting multi-scale features of the RGB image and the optical flow image; and performing modal guide enhancement fusion on the multi-scale features of the optical flow image by using the multi-scale features of the RGB image to obtain fusion features. The technical effects of improving the optical flow feature quality and enhancing the motion information reliability are brought, the problem of insufficient feature utilization caused by large optical flow noise and poor stability in multi-modal data is effectively relieved, and a high-quality cross-modal fusion basis is provided for subsequent saliency target detection.

The present application belongs to the field of computer vision, and provides an RGB-D saliency object detection method based on depth quality weighting, comprising the following steps: 1) obtaining an RGB-D dataset for training and testing the task, and defining the algorithm target of the present application; 2) constructing an RGB encoder for extracting RGB image features and a depth (Depth) image feature encoder; 3) constructing a cross-modal weighted fusion module, and guiding the weighted fusion of the extracted RGB image features and Depth image features through a depth quality evaluation mechanism guided by a weighting formula; 4) constructing a bidirectional scale correlation convolution mechanism for multi-scale feature extraction and fusion, so as to enhance the advanced semantic information of multi-modal features; 5) establishing a decoder to generate a saliency map P est ; 6) calculating the loss of the predicted saliency map P est and the manually labeled saliency object segmentation map P GT ; 7) testing the test dataset to generate a saliency map P est , and performing performance evaluation using evaluation indexes. The present application can effectively integrate complementary information from different modal images, and improve the accuracy of saliency object prediction in complex scenes.

The application provides a kind of synergistic salient object detection method and storage medium, the method is realized by salient feature enhancement and global information guidance, constructs synergistic salient object detection model, in down-sampling network, image feature is extracted by VGG16 backbone network, and the saliency of image feature is enhanced using coordination attention module, and dynamic convolution collaborative search module is used to search common salient object feature as synergistic feature, in up-sampling network, receptive field inflation technology is used to increase receptive field, and long-distance dependence information of image is obtained by non-local module, to optimize synergistic feature, and as the input of global information guidance fusion module, to reduce non-salient background interference. Finally, the whole synergistic salient object detection model is optimized by loss function. The method is fast in operation, and the final synergistic salient object prediction result is complete in structure and accurate in target.

The present application relates to the technical field of image processing, and provides an RGB-D salient object detection and semantic segmentation method and system. The RGB-D segmentation model module comprises an encoder and a decoder; the encoder comprises a plurality of RGB-D blocks; in each RGB-D block, based on an RGB feature map and a depth feature map, a cross-modal attention mechanism is used to obtain a cross-modal attention feature map, and after depth separable convolution is performed on the depth feature map, logical operation is performed on the RGB feature map to obtain a local enhancement feature map; the cross-modal attention feature map, the local enhancement feature map and a shortcut feature map of the RGB feature map are linearly processed to obtain an RGB output result and a depth output result; and the RGB output result and the depth output result are taken as the input of the next RGB-D block.

This application discloses a training method, apparatus, electronic device, and computer-readable storage medium for a video salient object detection model. Addressing the issues of insufficient multimodal fusion, temporal instability, and high label dependence, a two-stage training framework is proposed: The first stage uses cross-modal unsupervised contrastive learning to mine consistency and complementarity information between RGB and deep modalities, generating and iteratively optimizing salient object pseudo-labels to improve their quality; the second stage uses the optimized salient object pseudo-labels as supervision signals, selecting historical frames and adjacent frames to construct a reference set, training the target model through temporal feature fusion, and iteratively updating network parameters, enabling the model to obtain a stable representation in the time dimension, enhancing its ability to model long-term and short-term dependencies, suppressing dynamic interference, and maintaining target continuity. This method eliminates the need for manually labeled data, effectively reducing data costs through cross-modal contrastive learning and temporal feature fusion, while improving detection accuracy and robustness in complex scenes.

The present application relates to a kind of lightweight double-flow cross-modal interaction's RGB-D salient target detection method, comprising the following steps: step S1: data preparation, obtain the RGB-D dataset of this task, for training and testing, wherein part of NJU2K dataset and part of NLPR dataset are used as training set, the remaining part of NJU2K dataset and NLPR dataset, SIP dataset, STERE dataset and SSD dataset are collectively used as test set;Step S2: network model is built, including: step S21: the feature extraction main network construction of decoder, step S22: adaptive cross-modal fusion module ACM, step S23: multi-scale hollow attention module MSA;Step S24: decoder, step S25: loss function calculation, step S26: evaluation index;The present application has the advantages that: in the case where having lower model complexity, still can maintain higher computational efficiency, while processing multiple types of scene has stronger generalization and accuracy.

The application discloses a concrete slump detection method based on salient target detection, and specifically comprises the following steps: step 1, image preprocessing; step 2, multi-scale reuse salient target identification based on deep learning; and step 3, pixel difference conversion and result analysis output. By using the application, the problems that the existing detection methods rely on manual operation and subjective judgment are solved, the detection result is more accurate and stable, human resources are saved, the method system of the computer vision and artificial intelligence technical field is enriched, the development of the salient target detection field is supported, and a selection is provided for the concrete slump detection.

This invention discloses a salient object detection method based on a multi-scale dilated convolutional neural network. The method includes: extracting multi-scale features from the input image; inputting the multi-scale features into a dilated residual convolutional module to obtain fused features including contextual information of the multi-scale features; inputting the fused features into multiple channel attention modules to obtain multiple salient features; performing dimensionality reduction activation on each salient feature to generate a saliency map; and performing deep supervised training using a hybrid loss function that combines cross-entropy and cross-union loss. The method of this invention, based on a multi-scale dilated convolutional neural network, fully captures rich global and local semantic information in the image by using a dilated residual convolutional module, solving the problem of shallow encoder depth and insufficient information extraction. Simultaneously, the designed channel attention modules enable the network to focus on the target region, effectively improving the accuracy of object detection.

The invention provides a saliency target detection method, device and system and electronic equipment, and belongs to the field of computer vision. The method comprises the following steps: acquiring a first visible light image and a first thermal infrared image of a to-be-detected object; inputting the first visible light image and the first thermal infrared image into a pre-trained first model to obtain a first saliency target image of the to-be-detected object; the first model is an image fusion neural network model determined based on a second visible light image and a second thermal infrared image of the training object; the first model comprises a self-adaptive enhancement module, a coding and fusion module and a three-stream differential cooperative decoder. In conclusion, the technical scheme provided by the invention can progressively solve the technical problems of low detection precision, weak anti-interference capability, poor fusion effect and the like of the existing method layer by layer from three core links of input enhancement, feature fusion and decoding collaboration, improves the precision of saliency target detection, and can adapt to various application scenes.

The utility model relates to the technical field of SOD wearable glasses, in particular to a virtual reality (VR) terminal based on panoramic image salient target detection (SOD), which comprises a VR equipment body, connecting ends are fixed on two side walls of the VR equipment body, one end of each connecting end is fixedly connected with a connecting band through a clamping end, the other end of each connecting band is fixedly connected with an adjusting assembly, and the adjusting assembly is fixedly connected with the VR equipment body. A binding belt is fixed between the adjusting assemblies, the connecting end comprises a connecting frame and a clamping roller, the clamping roller is arranged in the connecting frame, a through hole is formed in the connecting frame, the connecting belt structure can be sleeved with a rotating rod structure through a sleeve structure to be connected and fixed, and the rotating rod structure is fixed to the positioning table. The driving rod-driving wheel-driven wheel-driven rod structure can be driven to rotate through the rotating block structure, the redundant bandage structure is convolved towards the driven rod, the folding effect is achieved, and the manual adjusting effect can be achieved through the rotating block structure on the outer side.