Patents
Literature
23results about How to "Improve target detection efficiency" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
Adaptive CFAR detection method based on scene classification
ActiveCN119540738AFalse alarm rate constantresolve interferenceCharacter and pattern recognitionCluster algorithmConstant false alarm rate
The invention provides an adaptive CFAR detection method based on scene classification, and the method comprises the steps: carrying out the scene classification of an input SAR image through employing a K-means clustering algorithm, carrying out the target detection of a pure sea area obtained through classification through employing VI-CFAR, and carrying out the CFAR detection of a coastal area through employing a significance model. According to the method, the problems that a traditional CFAR algorithm is high in false alarm rate and unstable in detection performance in port and land complex backgrounds are effectively solved, detection parameters are optimized according to the complexity of different scene backgrounds, and therefore it is ensured that the CFAR algorithm can still maintain the constant false alarm rate in the sea clutter background, and the robustness of target detection is remarkably improved. The ocean area and the land area in the image are effectively distinguished, and the land area is shielded. According to the technical scheme, the problem that a traditional CFAR detection algorithm is interfered by a complex land background when detecting ships on the sea surface is solved.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
SAR and optical image adaptive adversarial feature fusion target detection method
InactiveCN119478378AImprove target detection efficiencyquality improvementCharacter and pattern recognitionNeural learning methodsImage pairNuclear medicine
The invention discloses an SAR and optical image adaptive adversarial feature fusion target detection method, and relates to the field of deep learning image fusion and the field of target detection. Comprising the following steps: performing speckle filtering processing on an SAR image by adopting a Refine Lee filter to obtain a filtered SAR image; performing image registration processing on the filtered SAR image and the filtered optical image to obtain a registered SAR image and a registered optical image; performing adaptive feature fusion on the registered SAR image and optical image by using a channel and space attention mechanism and a double-discriminator generative adversarial network to obtain a target fusion image; and performing target detection on the target fusion image by using a position attention mechanism and a YOLOv8 algorithm to obtain a target detection result. The problem that target detection of a neural network in a complex scene SAR image or an optical image is difficult to achieve ideal detection performance is solved.
Owner:BEIJING INST OF REMOTE SENSING EQUIP
Lightweight service robot target detection method and device
ActiveCN119399438AImprove feature extractionImprove real-time performanceInternal combustion piston enginesBiological modelsSimulationService robot
The invention relates to the technical field of robot target detection, in particular to a lightweight service robot target detection method and device.The lightweight service robot target detection method comprises the steps that a lightweight service robot detection model is constructed, and the lightweight service robot detection model is constructed on the basis of YOLOv5n; a MobileViT module, a C3DSC module and a convolution triple attention module are introduced into a backbone network, the MobileViT module, the C3DSC module and a deformable convolution module are introduced into a neck network, and then the lightweight service robot detection model is used for carrying out target detection on collected images of a service robot. The lightweight service robot detection model constructed by the invention effectively improves the detection performance of a multi-scale target, especially a deformed object, has relatively high detection precision while ensuring high real-time performance of the model, and is suitable for target detection requirements of the service robot.
Owner:SUZHOU UNIV
A method for object detection based on image super-resolution
ActiveCN112508787BImprove target detection efficiencyreduce complexityGeometric image transformationCharacter and pattern recognitionPattern recognitionComputation complexity
A target detection method based on image super-resolution includes the following steps: step 1, sending the original image into a target detection network to obtain an image that needs super-resolution; step 2, using a super-resolution network to perform super-resolution on the output feature map to obtain a feature map of a larger size; step 3, re-cropping the feature map of step 2 into several small areas, and then searching for a target frame in each small area; step 4, comparing the feature map with the feature map obtained in step 3 to obtain a loss function of the entire network. The present invention improves the target detection efficiency of the entire network by using a super-resolution network to improve the resolution of the feature map or part of the original image, thereby reducing the complexity of super-resolution and the computational complexity of processing the super-resolution original image, and improving the real-time performance of the entire target detection task based on super-resolution.
Owner:PANJI TECH CO LTD
Target detection methods, apparatus, equipment and storage media
ActiveCN115797735Bimprove accuracyImprove target detection efficiencyNeural learning methodsPattern recognitionNetwork architecture
This application provides a target detection method, apparatus, device, and storage medium. The method includes: acquiring an image to be detected; inputting the image to be detected into a pre-trained target detection model to obtain a target recognition result for the image; wherein the target detection model is obtained by sequentially setting one of multiple detection models as a teacher model and the remaining models as student models, and training the student models based on the teacher model. Since the target detection model is trained from multiple detection models, it can absorb the correct detection capabilities of different detection models, thereby improving the accuracy of target detection. Therefore, there is no need to introduce a complex backbone network or a complex bottleneck layer into the target detection model; a high target detection accuracy can be achieved through a simple network architecture, and the relatively simple network architecture also improves the efficiency of target detection.
Owner:ZHEJIANG PECKERAI TECH CO LTD
Target detection method and device, electronic equipment and storage medium
The application discloses a target detection method and device, electronic equipment and storage medium, and belongs to the technical field of image processing. The target detection method comprises the following steps: acquiring a to-be-detected image, performing target detection on the to-be-detected image to obtain a target detection result, performing negative example filtering on the target detection result to obtain a detection result corresponding to a target user, and obtaining an image retrieval feature library corresponding to the target user by preprocessing and feature extraction on a negative example image determined for the target user. The application can obtain a detection result of a target detection event defined for the target user without separately customizing a target detection model for the target user, only needs to perform negative example filtering on the target detection result according to the image retrieval feature library corresponding to the target user, can improve the efficiency of target detection, and reduces the implementation cost.
Owner:IFLYTEK CO LTD
Radar ground-sea clutter environment multi-feature identification method
ActiveCN119355673AGuaranteed connectivityImprove robustnessWave based measurement systemsKnowledge based modelsFeature vectorGraphics
The invention relates to a radar ground-sea clutter environment multi-feature identification method, and belongs to the technical field of radar signal processing. According to the method, firstly, feature extraction is carried out on each data sequence, and normalization is completed; forming a feature vector by four feature values of each resolution unit, sending the feature vector to a random forest classifier to complete judgment, and setting the resolution unit which is judged to be sea clutter as 0; and the resolution unit for determining the ground clutter is set to be 1. And converting a 0 / 1 matrix obtained according to the judgment result into a grey-scale map, and taking the grey-scale map as a primary identification result. In order to improve the identification result, closed operation and hole filling are sequentially used for processing the primary identification result, the identification effect is improved, and a secondary processing result is obtained. The method has higher robustness and accuracy, and the extraction of the potential target area is beneficial to improving the target detection efficiency.
Owner:NAVAL AVIATION UNIV
A Passive Radar Moving Target Detection Method Based on Low-Earth Orbit Internet Satellite Radiation Source
ActiveCN119511329Bavoid error accumulationImprove target detection efficiencySatellite radio beaconingRadio wave reradiation/reflectionPassive radarThe Internet
This invention discloses a passive radar moving target detection method based on low-Earth orbit (LEO) internet satellite radiation sources. Applied to the field of passive radar moving target detection, it addresses the problem of difficulty in acquiring satellite signals due to non-cooperation when using LEO internet satellites as radiation sources. This invention first acquires the direct wave and echo signals from the LEO internet satellite; then, it performs synchronization preprocessing on the direct wave signal to obtain the synchronization sequence; next, it demodulates both the direct wave signal and the target echo signal into the OFDM symbol domain; within this symbol domain, it calculates the correlation between the direct wave and target echo signal matrices; then, through signal processing of the correlation calculation results, it extracts the target bistatic distance and Doppler information from the correlation results; finally, it completes target detection based on this information.
Owner:YANGTZE DELTA REGION INST (QUZHOU) UNIV OF ELECTRONIC SCI & TECH OF CHINA
Target detection method and device, computer device and storage medium
The application discloses a target detection method and device, computer equipment and a storage medium. The method comprises the following steps: taking the first branch network of a target detection model as a target network; inputting a to-be-detected image into the target network to obtain a target detection result and a confidence thereof; determining a score of the to-be-detected image according to the confidence of the target detection result; if the score is less than a threshold corresponding to the target network, taking the next branch network of the target network as a new target network, returning to the step of determining the score of the to-be-detected image until the score is greater than the threshold corresponding to the target network; and taking the target detection result output by the target network as a final detection result of the to-be-detected image. The above scheme can dynamically infer the target detection process of the to-be-detected image, and can improve the efficiency of target detection while ensuring the accuracy of target detection.
Owner:INDUSTRIAL AND COMMERCIAL BANK OF CHINA
Target detection method and device, electronic equipment and storage medium
ActiveCN116610979BTarget detection implementationSolve the problem of time-consuming calculation of intersection and merge ratioNeural learning methodsAlgorithmGrid based
Owner:UISEE TECH BEIJING LTD
Target detection method, device and electronic device based on large model
ActiveCN119339066BImprove robustnessImprove generalization abilityCharacter and pattern recognitionEnergy efficient computingAlgorithmEngineering
This application proposes a target detection method, apparatus, and electronic device based on a large model, relating to the fields of target detection and welding technology. The method includes: inputting multimodal object data into a large model, the large model including a first encoder; performing at least one of coordinate transformation and dimension transformation on the first modality object data using the first encoder to obtain target transformation data for the first modality; fusing the target transformation data of the first modality with object data of a second modality using the first encoder to obtain fused data; and performing target detection based on the fused data using the remaining networks in the large model other than the first encoder. Therefore, multimodal data can be used for target detection, improving the robustness, generalization, and accuracy of target detection. The first encoder can be used to perform coordinate transformation and / or dimension transformation on object data of a certain modality to obtain target transformation data for that modality, thereby achieving multimodal data fusion.
Owner:BEIJING XIAOYU INTELLISYS CO LTD
Real-time weak and small moving target detection method based on space-time parallel coupling robust principal component analysis
ActiveCN119540534AImprove target detection efficiencyCharacter and pattern recognitionComplex mathematical operationsDecomposition problemGranularity
The invention provides a space-time parallel coupling robust principal component analysis real-time weak and small moving target detection method, device and equipment and a medium. The method comprises the following steps: constructing a space-time data matrix based on to-be-detected image data; constructing the coarse-grained sparse decomposition problem based on the to-be-solved rough low-rank matrix and the to-be-solved extreme sparse matrix, and constructing the fine-grained sparse decomposition problem based on the to-be-solved extreme low-rank matrix and the to-be-solved rough sparse matrix; respectively solving the coarse granularity sparse decomposition problem and the fine granularity sparse decomposition problem based on a Lagrange multiplier algorithm to obtain a rough low-rank matrix and an extreme low-rank matrix; and processing the rough low-rank matrix and the extreme low-rank matrix by using an edge continuity clustering detection algorithm to obtain a weak and small moving target detection result, so that a background modeling process can be directly abandoned, the target can be directly detected from the sparse matrix, and the target detection efficiency is improved.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Coherent accumulation method, target detection method, device, equipment and radar system
PendingCN121763236Aimprove accuracyimprove signal-to-noise ratioWave based measurement systemsRadar systemsScan angle
The embodiment of the invention provides a coherent accumulation method, a target detection method, devices, equipment and a radar system, and the method comprises the steps: obtaining a point corresponding to a to-be-confirmed point in a next frame for the to-be-confirmed point of a current frame; and based on the angle of the to-be-confirmed point in the current frame, performing coherent accumulation on the data of the plurality of antenna channels of the to-be-confirmed point in the point corresponding to the next frame to obtain a coherent accumulation value of the to-be-confirmed point in the point corresponding to the next frame. According to the invention, coherent accumulation is carried out on the point corresponding to the to-be-confirmed point in the next frame by using the angle of the to-be-confirmed point in the current frame, the scanning angle during coherent accumulation can be directly determined, variable search or traversal does not need to be carried out, the calculation amount of coherent accumulation is reduced, and the efficiency of coherent accumulation is improved.
Owner:BYD CO LTD
Passive radar moving target detection method based on low-orbit internet satellite radiation source
ActiveCN119511329Aavoid error accumulationImprove target detection efficiencySatellite radio beaconingRadio wave reradiation/reflectionTelecommunicationsPassive radar
The invention discloses a passive radar moving target detection method based on a low-orbit internet satellite radiation source, is applied to the field of passive radar moving target detection, and aims to solve the problem that satellite signals are difficult to capture due to non-cooperation of the radiation source when a low-orbit internet satellite is taken as the radiation source. The method comprises the following steps: firstly, acquiring direct wave and echo signals from a low-orbit internet satellite; performing synchronous preprocessing on the direct wave signal to obtain a synchronous sequence in the signal; the direct wave signal and the target echo signal are demodulated to an OFDM symbol domain; in the symbol domain, correlation calculation is carried out on a direct wave and a target echo signal matrix; then, through signal processing of a correlation calculation result, target double-base distance and Doppler information are extracted from the correlation result; and finally, target detection is completed based on the information.
Owner:YANGTZE DELTA REGION INST (QUZHOU) UNIV OF ELECTRONIC SCI & TECH OF CHINA
Frequency agility radar target detection method
PendingCN119395637AImprove refactoring efficiencyImprove target detection efficiencyRadio wave reradiation/reflectionICT adaptationAlgorithmSide lobe
A frequency agility radar target detection method comprises the following steps: firstly, designing a self-adaptive iteration step length based on SCCS, and solving the problem that the iteration step length cannot be adjusted in time when the residual error change of a traditional SAMP algorithm is small, so that the signal recovery efficiency is reduced; and then, a grid correction method combining a regularization staged backtracking idea and gradient descent optimization is adopted, so that stray sidelobe energy can be effectively suppressed under the condition of lacking priori information of a sparseness level. According to the method, the problem that phase-coherent accumulation gain loss is increased due to the fact that the step length is fixed in a traditional SAMP method is solved, and the target detection performance can be improved.
Owner:SHANGHAI RADIO EQUIP RES INST
An image target detection method and system based on improved YOLOv8
The application discloses an image target detection method and system based on an improved YOLOv8, and the image to be detected is input into a trained target detection network to obtain a detected target; wherein all C2f modules in a backbone network of YOLOv8 are replaced by novel cyclic vision modules in the target detection network; wherein the novel cyclic vision modules comprise convolution sub-modules, local self-attention sub-modules, first multi-layer perceptron sub-modules, global self-attention sub-modules, second multi-layer perceptron sub-modules and feature pyramid multi-scale fusion sub-modules connected in sequence, and the output of each sub-module is further connected to the next sub-module through a residual connection. The application has higher efficiency.
Owner:HUAIYIN INSTITUTE OF TECHNOLOGY
Target detection methods and equipment
ActiveCN115546708BImprove target detection efficiency
This disclosure provides a target detection method and apparatus. The method includes: acquiring an image to be detected; detecting targets in the image using a first model to obtain an attention map and a density map of the image; and generating a target detection map of the image based on the attention map and the density map, wherein the target detection map includes location markers of the targets in the image. The first model is a lightweight deep learning model trained based on sample data, a second model, and a first loss function. The first loss function includes a distillation loss function that reflects the distillation loss between the first and second models, and the model size of the second model is larger than that of the first model. Therefore, by using a lightweight deep learning model trained based on knowledge distillation, the efficiency of target detection in the image is improved.
Owner:BEIJING ZITIAO NETWORK TECH CO LTD
Target detection method, target detection device, equipment and storage medium
PendingCN119251474Aimprove accuracyImprove target detection efficiencyInternal combustion piston enginesBiological modelsMedicineImage detection
The embodiment of the invention provides a target detection method, a target detection device, equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring a training sample set, and adjusting a sample image based on a target size label and a preset target size to obtain an adjustment target and a sample adjustment image; dividing the adjustment target based on a preset target size to obtain an adjustment small target and an adjustment large target; detecting the sample adjustment image based on the initial detection model to obtain a first prediction target category; calculating a first small target loss contribution value based on the first prediction target category; determining a first small target area proportion based on the small target total area of the adjusted small target and the large target total area of the adjusted large target; and adjusting the initial detection model based on the first small target loss contribution value and the first small target area proportion to obtain a target detection model so as to perform target detection on the target image. According to the embodiment of the invention, the target detection efficiency can be improved on the basis of ensuring the detection precision.
Owner:PING AN TECH (SHENZHEN) CO LTD
Snowfield target detection method for self-driving automobile
PendingCN119540710Aincrease dependenceimprove accuracyCharacter and pattern recognitionNeural learning methodsData setFeature learning
The invention discloses a snowfield target detection method for an autonomous vehicle. The method comprises the following steps: step 1, acquiring a snowfield data set; 2, a target detection model YOLOv8 is improved, and a multivariate feature learning module (MFL), a relevance space information enhancement module and light-weight easily-fused C2f (LFC2f) are provided; 3, training the improved model and obtaining a test result; and 4, carrying out an ablation experiment, and verifying the effectiveness of each module and the optimal position of the MFL module. An MFL module and an information enhancement module are innovatively provided on a YOLOv8 model, and an LFC2f module is added. According to the method, the trunk part of the YOLOv8 is improved, a C2f module at a specific position is replaced with an MFL module, an excellent detection effect is achieved with the lowest calculation cost, LConv is introduced to the remaining C2f modules, an LFC2f module is proposed, and the model is lighter while the precision is ensured. And an information enhancement module is provided to enhance the attention on spatial features, so that the model can better adapt to a complex snowfield environment.
Owner:CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Steel cable surface defect detection method based on improved SSD algorithm
InactiveCN119515789Aimprove accuracyImprove real-time performanceImage enhancementImage analysisData setAlgorithm
The invention discloses a steel cable surface defect detection method based on an improved SSD algorithm, and the method comprises the steps: S1, constructing a steel cable surface defect data set which comprises a plurality of steel cable images and defect types corresponding to the steel cable images; s2, constructing a steel cable surface defect detection neural network, and training the steel cable surface defect detection neural network by using the data set to obtain a steel cable surface defect detection model; the steel cable surface defect detection neural network is constructed based on an SSD network, RestNet18 is introduced to serve as a backbone of the SSD network, a one-way feature fusion block and an improved CBAM attention module are introduced, and meanwhile, Focus serves as a loss function. And S3, acquiring a steel cable image in real time, and inputting the steel cable image into the steel cable surface defect detection model to obtain a corresponding defect category. According to the steel cable surface defect detection method based on the improved SSD algorithm, the steel cable production efficiency can be effectively improved, and the labor cost is reduced.
Owner:NANTONG UNIV
Multi-station radar high-speed target detection method based on axis rotation and dual-core radon cuboid phase
PendingCN122172174AAvoid computationally expensive pitfallsImprove target detection efficiencyRadio wave reradiation/reflectionRadarTarget signal
This invention proposes a multi-station radar high-speed target detection method based on axis rotation and dual-core Radon cubic phase. The implementation steps are as follows: range travel compensation is performed on the signal after second-order Keystone transform based on an improved axis rotation method; Doppler diffusion compensation is performed on the potential target signal based on the dual-core Radon cubic phase function; the data correlation results between the measured values and the target are calculated, and the multi-station radar high-speed target detection results are obtained. In the range travel compensation of the second-order Keystone transform signal, this invention only rotates the fast time axis of each range block signal, improving the two-dimensional parameter search for range and velocity into a one-dimensional rotation angle search, thus reducing the computational load; the Doppler diffusion compensation of the potential target signal based on the dual-core Radon cubic phase function transforms the high-dimensional parameter search into an instantaneous frequency change rate search, improving detection efficiency and detection probability.
Owner:XIDIAN UNIV
Method and device for assisting target detection using video coding information
ActiveCN113794886Bsave resourcessmall amount of calculationDigital video signal modificationVideo encodingEngineering
The present invention discloses a method and device for assisting target detection using video coding information, including: determining a target reference frame for a video frame to be detected, the target reference frame including several sub-window blocks; for each sub-window block: determining a reference position of the sub-window block in the video frame to be detected; determining a reference value of the reference position; determining an overall reference value of the video frame to be detected based on the reference value of each sub-window block; searching for a target window in the video frame to be detected based on the overall reference value within the constraint range of a target deformation function; and performing target detection based on the target window. The method disclosed in the present invention can effectively reduce the amount of calculation, improve the efficiency of target detection, and save hardware resources and reduce power consumption and delay as a whole.
Owner:RONG MING MICROELECTRONICS (JINAN) CO LTD
Target detection method and device, electronic equipment and machine readable storage medium
The application provides a target detection method and device, electronic equipment and machine readable storage medium. The method comprises: voxelizing input point cloud data, and performing voxel feature extraction to obtain voxel features of the input point cloud data; according to the voxel features, classifying non-empty voxels corresponding to the input point cloud data into foreground and background, and determining the offset of each foreground voxel relative to a target center point; according to the offset of each foreground voxel relative to the target center point, clustering the foreground voxels to obtain a clustered target cluster; and according to the target cluster, generating a target detection frame by using a cluster-based target detection structure. The method can improve target detection efficiency and improve target detection performance.
Owner:HANGZHOU HIKVISION DIGITAL TECHNOLOGY CO LTD