143results about How to "Improve object detection performance" patented technology

The invention provides a PCBA surface defect detection method and system based on image recognition, and the method comprises the steps: collecting and constructing a multi-class PCBA overall defect image data set, and carrying out the preprocessing; constructing a pre-training neural network model, and classifying the preprocessed PCBA overall defect image data set; constructing a local target identification method based on a YOLOX network according to an overall defect classification result; and carrying out local defect detection on the PCBA surface based on a local target identification method. According to the method, the overall classification and local detection strategies are comprehensively applied to reduce the burden of the model, and the detection precision and speed are improved. And an attention mechanism is introduced to improve the performance and generalization ability of the model. Data enhancement, consistency processing, classification labeling and the like are carried out on an image data set, and the defect detection effect of the model is improved. And multiple optimization strategies such as structure improvement, loss function improvement and pruning lightweight are adopted for the network model, so that the image target detection performance is effectively improved.

The invention discloses a multi-modal sensor information fusion-based target detection method and system. The method comprises the steps of obtaining a visible light image and an infrared image of a preset area; based on a depth estimation network, respectively extracting depth information of the visible light image and the infrared image to obtain a depth estimation map after fusion of the visible light image and the infrared image; feature information of the visible light image and the infrared image is extracted based on a feature extraction network, and scene perception features of the depth estimation map are extracted; and carrying out feature fusion on the feature information of the visible light image and the infrared image and the scene perception feature of the depth estimation map by using a feature fusion network to obtain a depth estimation fusion image in a preset region, and carrying out to-be-recognized target detection on the depth estimation fusion image based on a target detection network. Through complementary characteristics of visible light and infrared images, depth information and scene perception capability of the images are enhanced based on a depth estimation network and a feature extraction network, and image target identification accuracy and robustness are improved.

The invention provides a multi-modal target detection method and device and a multi-modal recognition system. The method comprises the steps that a scene image is acquired, and the scene image is an image of a current environment acquired by adopting image acquisition equipment; acquiring a point cloud image, the point cloud image being an image of point cloud data of the current environment acquired by using a laser radar; according to the scene image and the point cloud image, fusion is carried out to obtain a target image, and the fusion mode at least comprises image fusion and / or feature fusion; and performing target identification on the target image to obtain a target object in the target image. According to the scheme, the problem of low accuracy of target detection based on single data in the prior art is solved.

The invention discloses a rainy day target detection method based on paired image data generation and knowledge distillation, and belongs to the technical field of automatic driving target detection. The method comprises the following steps: acquiring a real image of a road scene and a semantic segmentation mask; inputting the semantic segmentation mask and the weather condition text information into a trained improved diffusion model to obtain a road scene generation image under a rainy day condition; introducing a CoT module and an ODConv module into the target detection model for optimization, and then improving the target detection model by adopting a dynamic weighted aggregation loss function to obtain an optimized target detection model; performing knowledge distillation to obtain a rainy day target detection enhancement model; and performing target detection by using the rainy day target detection enhancement model. The method provided by the invention is excellent in performance in a rainy day scene, has good generalization ability, can provide higher-precision target detection in various complex scenes, and effectively improves the detection accuracy and stability.

The invention discloses a monitoring video target detection method based on multi-frame high-frequency difference enhancement, and the method comprises the following steps: 1, firstly, selecting a series of monitoring videos, carrying out the segmentation and frame extraction of the monitoring videos, and sampling one frame every ten frames as a training set, carrying out data preprocessing and enhancement operation on the input continuous image frames; and 2, constructing a multi-frame image reconstruction module to reconstruct an image according to the low-layer feature maps of the current frame and the reference frame, and optimizing parameters of the reconstruction module by calculating mean square error (MSE) loss between the original image and the reconstructed image. According to the monitoring video target detection method based on multi-frame high-frequency difference enhancement, a continuous frame high-frequency difference chart and a self-adaptive fusion enhancement feature strategy are ingeniously combined, high-frequency motion features are extracted and fused through reconstruction of continuous frames, and a powerful target detection framework is constructed; the target detection performance in the monitoring video can be obviously improved.

The invention provides a coal gangue multi-scale detection method and system based on a YOLOv8 network, and the method comprises the steps: enabling a CBAM attention mechanism to be embedded in a whole network core module C2f, and improving the small target detection capability; secondly, a fast spatial pyramid pooling module is adopted in the backbone network, more front and back features are fused, interaction of feature information is enhanced, and the multi-scale detection capability is further improved; then, through a bidirectional feature fusion module with more jump connections, the multi-scale detection capability is increased, and small target features are prevented from being lost due to network depth; and finally, convolution blocks used for feature fusion in the detection head are combined for weight reduction, regression loss is set as EIoULoss, and errors caused by the fact that the length-width ratio of a prediction frame is consistent with that of a real frame are avoided. By means of the target network model, coal gangue with the size fraction ranging from 6 mm to 50 mm can be effectively detected, cross-size-fraction detection is achieved, and the target network model can be effectively deployed in coal gangue separation equipment.

The invention discloses a remote sensing target detection method based on a target feature enhanced multi-scale fusion network, and the method comprises the steps: constructing a multi-layer feature aggregation module as a main feature extraction module, and better extracting the target features of each category; a multi-channel feature fusion module is constructed, the network layer parameter quantity is reduced, a cross-stage hierarchical structure is adopted to design an MCFCSP module, richer gradient combination and feature expression are realized, and the network feature fusion capability is enhanced; by adopting a top-down and bottom-up feature fusion strategy, information interaction between different levels of features is promoted; fractional Fourier transform convolution is constructed, and parameters are adjusted according to the characteristics of small targets and the change of scenes to adapt to small targets of different types, different sizes and different complexities. The method provided by the invention has the characteristics of high detection precision, low calculation complexity, strong feature representation capability and the like, can effectively improve the detection precision of the small target, and further improves the detection performance of the small target of the remote sensing image.

The invention relates to a sea surface floating small target detection method based on multi-modal image fusion. Comprising the following steps: acquiring a plurality of image pairs, preprocessing the acquired image pairs, and constructing an image sample data set by using the preprocessed image pairs; enhancing the contrast and detail information of the infrared polarization image by using an optimization algorithm; extracting low-pass sub-band and band-pass directional sub-band information of different modal images by using multi-scale transformation, determining fusion weights of the two parts according to the three-channel simplified pulse coupling neural network, and finally obtaining a final fusion result by using inverse transformation of the multi-scale transformation; and inputting the three-mode fusion image into a fixed detection network to obtain a target detection result. According to the method, the relative effect of the infrared polarization, the infrared intensity and the visible light image is effectively measured, the contrast and detail information of the infrared polarization image are effectively enhanced, the fusion feature is enhanced, and better fusion image quality and target detection performance are achieved.

The invention provides a novel YOLOv10-based safety belt detection method and system, and the method comprises the steps: obtaining image data, and marking the image data, namely marking the position / wearing state of a safety belt; constructing a detection model based on a novel YOLOv10 network, namely replacing partial standard convolution Conv of the Backbone network with local attention regularization convolution; using a separable convolution star module to replace an original feature extraction module of the Backbone network; a CARAFE module is used for replacing up-sampling operation in a Neck network, multi-scale space attention dilation convolution fusion modules are additionally arranged before three sets of feature maps output by the Neck network, and then the feature maps are output to a Head network; and training a detection model by using the labeled image data to obtain a safety belt detection model. When the novel YOLOv10 network is used for safety belt detection, the detection efficiency and performance can be remarkably improved.

The invention discloses an unmanned aerial vehicle video target detection method and system based on space-time correlation, and the method comprises the steps: S01, inputting the video image data of an unmanned aerial vehicle into a feature extraction network, and obtaining an initial feature sequence; s02, embedding the initial feature sequence into a spatial position, performing deep convolution processing, performing space-time modeling by using a state-space equation, and fusing with the proportionally adjusted initial feature through residual connection to form a space-time feature; s03, inputting the spatial-temporal features into an attention network with residual connection, setting different weights according to appearance similarity, and fusing the spatial-temporal features through the residual connection; s04, performing feature fusion on the initial features and the spatio-temporal features after foreground attention processing; and S05, inputting the space-time fusion feature to a detection head to obtain a target detection result and outputting the target detection result. The method has the advantages of being simple in implementation method, few in needed computing resources, high in real-time performance and detection precision, high in expandability and the like.

The invention discloses a 3D target detection method and system based on binocular vision and open vocabularies, and relates to the technical field of target detection. The method comprises the following steps: calculating pixel features of each image by adopting an open vocabulary 2D detection model; pixel features of the left view and the right view are aggregated to each three-dimensional point; obtaining a parallax estimation map based on the pixel features of the left and right views; introducing a time aggregation guide parallax consistency constraint to obtain a depth parallax estimation map; converting the depth parallax estimation map into a 3D point cloud, and extracting features to obtain 3D features; and carrying out correlation integration on the text features, the 2D fusion feature vectors and the 3D features by utilizing object-level image guide detection loss, object-level cross comparison alignment and object-level self-comparison learning to obtain a three-dimensional target detection model, realizing three-dimensional target detection and distinguishing different target objects. According to the invention, accurate detection and distinguishing of the target can be realized, so that the flexibility and universality of 3D target identification are improved.

The invention relates to a dynamic context sensing pyramid network for infrared weak and small target detection and an infrared weak and small target detection method. Firstly, a dynamic context filtering module is used for adaptively capturing different size information of an input image to improve the feature representation capability of a target; secondly, a dynamic pyramid context module which is represented by multi-scale features generated by a plurality of dynamic context filtering modules is constructed, and the module can dynamically acquire multi-scale context information, so that more semantic information of the infrared small target is reserved in a deep network. And finally, realizing exchange of feature information of the same layer and establishment of association between different layers through a designed random attention fusion module, and ensuring fusion of deep semantic information and shallow spatial position information, thereby improving detection performance. The method can adapt to various complex backgrounds, reduces the false alarm rate and false alarm rate in actual detection, and has better target detection capability, background suppression capability and stronger robustness.

The invention relates to the technical field of radar weak target detection, in particular to a radar weak target detection method and device based on deep learning. According to the technical scheme, the method comprises the following steps: data preprocessing and enhancement: receiving an original echo signal of a radar, generating three-dimensional time frequency-wavelet coefficient spatial data by combining time frequency analysis with wavelet transform, and expanding a data set in multiple modes; and feature extraction network construction: constructing a 3D-CNN and LSTM fused network, wherein the 3D-CNN has a convolution operation with a variable convolution kernel size. According to the method, the data are richer and more comprehensive through time-frequency analysis in combination with wavelet transform and data enhancement, and multi-dimensional features can be accurately captured through 3D-CNN and LSTM fusion and an adaptive convolution kernel; and the training effect is ensured through the adaptive learning rate and the early stop method, and the accuracy and adaptability of weak target detection are jointly improved.

The invention relates to the technical field of signal processing, and provides an airborne radar multipath clutter suppression method and device. The method comprises the following steps: calculating test statistics of each training sample in a sample set, and selecting a training sample corresponding to a median of the test statistics as a reference sample; calculating the mahalanobis distance between the reference sample and each training sample, carrying out the non-uniform measurement of each training sample through employing the mahalanobis distance, obtaining a uniform training sample through screening, and determining a to-be-detected unit sample where the multipath clutter is located; constructing an air-space domain clutter covariance matrix of the to-be-detected unit sample according to the to-be-detected unit sample, calculating the rank of the air-space domain clutter covariance matrix so as to construct a multi-stage Wiener filter and a blocking matrix, and constructing an air-space domain adaptive weight vector according to the multi-stage Wiener filter and the blocking matrix; and performing space-time joint processing according to the space-space domain adaptive weight vector to obtain an output signal. According to the invention, the target detection performance of the airborne early warning radar in the multipath clutter environment of the complex mountainous area is improved.

The invention relates to a multi-scale twin hyperspectral target detection method and system, and belongs to the technical field of deep learning. The method comprises the following steps: S1, preparing initial target and background data of a hyperspectral image; s2, performing adversarial sample generation on the hyperspectral remote sensing image to obtain training sample data; s3, inputting the generated sample into a multi-scale twin target detection network for training; and S4, after training is completed, performing target detection on the test sample to obtain a result. The performance of the method is superior to that of other hyperspectral image target detection methods, the method can generate more sufficient and more diversified training samples, and the method has advantages in the aspect of background suppression compared with other methods.

The invention discloses a ship target detection method and system based on a large-kernel multi-scale attention mechanism. The ship target detection method comprises the steps of obtaining a to-be-detected ship target image; performing target detection on the to-be-detected ship target image by using a target detection neural network based on a multi-scale fusion strategy; in the target detection neural network, a detection task is divided into bounding box prediction, category prediction and object prediction, a large-kernel multi-scale attention module is integrated, the large-kernel multi-scale attention module firstly adopts 5 * 5 depth separable convolution to integrate local features, and then a two-dimensional large convolution kernel (k, k) is decomposed into cascaded horizontal depth separable convolution (1, 2); the method comprises the following steps of: extracting multi-scale information (k, k) and vertical depth separable convolution (k, 1), and weighting channel local features extracted by Ghost Module by utilizing the extracted multi-scale information; and the weighted features are deeply fused through the Ghost Module again, so that the accuracy of ship detection is further improved.

The invention relates to a traffic small target detection method and device for a high-speed driving vehicle, equipment and a medium, and relates to the field of image processing, and the method comprises the steps: inputting a traffic scene image frame into a second traffic small target detection model which is trained to a convergence state, and carrying out the detection of a traffic scene image frame in a plurality of multi-layer stacked recursive Star Block modules of a StarNet lightweight network, performing element-by-element multiplication on image features corresponding to the traffic scene image frames by adopting star operation to exponentially increase implicit feature dimensions in the traffic scene image frames so as to determine high-dimensional image features of the traffic scene image frames; and inputting the high-dimensional image features into a detection head network in a traffic small target detection model to obtain a plurality of traffic small targets in the traffic scene image frame so as to complete traffic small target detection of the high-speed driving vehicle. According to the method, the small traffic target can be accurately and quickly detected in a traffic scene in high-speed driving, and higher target detection performance can be realized in a resource-limited environment such as high-speed driving vehicles.

The invention belongs to the technical field of image processing, and particularly relates to a dual-light image target detection method under the view angle of an unmanned aerial vehicle, and the method comprises the steps: constructing a dual-light image target detection model which comprises a dual-light feature extraction and fusion network, a feature pyramid network and a rotating target detection head network; preprocessing the visible light and infrared light image data under the view angle of the unmanned aerial vehicle; inputting the preprocessed image pair into a dual-light feature extraction and fusion network to obtain fusion feature maps of different levels; inputting the fusion feature maps of different levels into a feature pyramid network, and outputting a multi-scale fusion feature map; the multi-scale fusion feature map is input into a rotating target detection head network, regression is carried out to obtain a rotating surrounding frame, a category and a confidence coefficient of the target under the view angle of the unmanned aerial vehicle, and the target detection capability in a complex environment is enhanced by a dual-light feature extraction and fusion network; the rotating bounding box regression strategy solves the problem that an existing horizontal bounding box is inaccurate in positioning when the target rotates.

The invention provides a two-stage generalized small sample target detection method and system based on self-supervision, and relates to the technical field of deep learning, and the method comprises the steps: defining a potential knowledge container to store all potential knowledge, and defining a prototype container to store prototypes of various candidate boxes; dividing the training data set into a base class set and a new class set; the self-supervised category domain carries out pre-training and fine tuning on the detection model based on the potential knowledge of the base category set and the new category set; the self-supervised detection frame field carries out pre-training and fine tuning on the detection model based on the potential knowledge of the base class set and the new class set; and inputting a to-be-detected image to the detection model, obtaining features through the feature extractor, performing potential knowledge search and segmentation on the to-be-detected image based on the potential knowledge container to obtain a relationship between the to-be-detected image and each potential knowledge, and obtaining a candidate box category. According to the method, detection model training is carried out in the category domain and the detection frame domain by using self-supervised learning based on potential knowledge, and the method has good target detection capability for both a base class and a new class.

The invention relates to a finger key point detection method for a factory operation safety system. The method comprises the following steps: acquiring input data; performing target detection and key point detection on the input data through a target detection network; the target detection network comprises a network backbone structure, a neck structure and a detection structure; the detection structure comprises a target detection head and a key point detection head; the key point detection head comprises a first convolution layer, a convolution and self-attention fusion module ACmix and a second convolution layer which are connected in sequence. The method has the advantages that the target detection network is optimized through the convolution and self-attention fusion module ACmix, the advantages of the convolutional neural network in the aspect of capturing image local features are reserved, and the perception ability of the model to global image semantics is remarkably enhanced.

The invention provides a method for identifying plaques in a coronary artery CT angiography image based on deep learning. The method comprises the following steps: firstly, calculating a coronary artery center line of a coronary artery CT angiography image, performing multi-angle curved surface reconstruction of the image based on the center line, and then performing fusion processing to realize image quality improvement; and then, constructing a coronary heart disease plaque detection network based on an attention mechanism, training the network by using the fused image to obtain a more effective detection network, and detecting coronary artery plaques in the image by using the trained detection network. According to the method, the problem of low accuracy caused by angle limitation in a single curved surface reconstruction image can be effectively solved, and the accuracy of plaque recognition in the coronary artery CT angiography image is remarkably improved.

The invention provides a multi-kernel SAR target detection method based on adaptive convolution, which comprises the following steps: firstly, receiving an input SAR image, sending the SAR image into a feature extraction network, after each convolution layer, using a SiLU activation function to introduce nonlinear transformation and enhance the expression ability of a model, and finally outputting an accurate target detection result. Comprising detailed information such as position, category and direction of the target. According to the method, the limitation of a traditional SAR target detection method in a complex environment is effectively solved, and the target detection performance is remarkably improved. According to the method, the challenge of multi-scale target detection is effectively dealt with, the accuracy of target recognition under a complex background is improved, the robustness of the model is remarkably enhanced, the performance of SAR image target detection is greatly improved, and the method has important value and wide application prospects in practical applications such as military reconnaissance, disaster monitoring and earth observation.

The invention belongs to the technical field of radars, and discloses a radar clutter trace point screening method using target short-time motion characteristics. The invention provides a radar clutter trace point screening method based on target motion characteristic parameter constraint and a short-time stability principle from trace point layer research on the basis that target motion generally has continuous and stable space-time relevance and clutter trace points appear randomly. According to the method, a set of clutter trace point screening method rules is provided based on target motion characteristic parameter constraints and short-term stability of target motion by only utilizing time and position basic information carried by trace points, clutter trace points can be effectively eliminated in a dense clutter background, target trace points can be effectively extracted, and the target trace points can be effectively extracted. And an effective technical support is provided for radar weak target detection and tracking under the clutter background.

The invention provides a target detection method for a communication and perception integrated system, which aims at the target detection problem of a multi-antenna ISAC system taking communication as a center, adopts virtual local echoes at a single-antenna target detection receiver to reduce the influence of a target direction on the echoes, smooths the influence of the direction on the echoes, and improves the target detection accuracy. Therefore, the interference of direction information on target detection is removed, and the target detection performance of the communication perception integrated system is improved. In order to further solve the problem of detection performance reduction caused by random communication signals and random additive noise, multiple VLEs are combined for target detection, and the detection capability is enhanced. Numerical simulation shows that compared with a correlation or energy detection method, the method has obvious advantages in a communication-centered multi-antenna ISAC system.

The invention provides a communication perception integrated waveform design method for the Internet of Vehicles, which belongs to the technical field of wireless communication, and can realize peak performance, enhance radar capability and ensure efficient communication by minimizing the weight sum of multi-user interference, the similarity between a design waveform and a reference waveform, a Cramer-Rao bound and an ISAC waveform. According to the method, optimization with the total transmission power and the peak-to-average power ratio as constraints is adopted, a non-convex problem is converted into a convex problem, an alternating direction multiplier method algorithm is adopted for solving, and a simulation result shows that the ISAC waveform has good communication and target detection capability, and the V2X spectrum utilization rate can be improved. According to the method, a target can be detected during communication with a downlink user, the sum of MUI, CRB and waveform similarity is minimized, weighing parameters are introduced, flexible weighing between communication and sensing is achieved under the constraint of the total power and the peak-to-average power ratio, the method better adapts to a V2X high-dynamic scene, and mutual interference caused by separation of traditional sensing and communication functions is reduced.

The invention relates to a turnout detection method based on computer vision and an intelligent monitoring pile. In order to improve the detection precision, an improved YOLOv8 model is provided, an original path aggregation feature pyramid network structure is jointly improved by using a space-depth convolution module (SDC) and a feature enhancement-fusion module (FEF) in a check layer of the network, and a small target detection enhancement pyramid structure is provided to enhance the small target detection capability. In addition, an improved loss function with a scaling factor and an auxiliary bounding box is used to further enhance the detectability of the model. Compared with the prior art, the method has higher accuracy and robustness.

The invention discloses an active and passive composite imaging method based on noise waves and a radar system, relates to the technical field of radar systems and target detection, and solves the problem that active imaging and passive imaging cannot be simultaneously realized by using one set of system due to different transmitting and receiving systems of an active system and a passive system. The method comprises the following steps: designing noise wave modulation, obtaining noise wave modulation information, and obtaining original data of an observation scene; performing pixel division on the original data according to the pixel size, the sampling frequency and the scanning speed; obtaining a correlation value and a mean value of the corresponding pixels according to the original data and noise wave modulation information in the divided pixels; obtaining active information data and passive information data according to the correlation value, the mean value, the active information and the passive information; and according to the active information data and the passive information data, obtaining an active information image and a passive information image, and completing imaging. The method is suitable for typical target detection scenes of sea surface, air and land.

The invention discloses an airborne radar moving target detection method and system based on intelligent sample selection, and the method comprises the steps: obtaining space-time echo signals of radar distance units, including space-time echo signals of different distance units and space-time echo signals of uniform distance units; constructing a training data set through the space-time echo signals of the uniform distance units; constructing a complex memory enhanced auto-encoder, and inputting the training data set into the complex memory enhanced auto-encoder to realize off-line training of the auto-encoder; inputting space-time echo signals of different distance units into the trained complex memory enhanced auto-encoder to obtain a reconstruction error; and selecting uniform distance units according to the reconstruction error, and detecting and removing polluted distance units. According to the method, the better sample selection performance can be obtained under the condition of reasonable operation complexity, and the target detection performance is better.

The invention discloses a vehicle-mounted dual-light fusion method and system. Comprising a network construction module, a training set construction module, a network training module and a fusion image generation module. According to the dual-optical fusion network based on the advanced perception task, the fusion image generation module and the target detection module are adopted, and the target detection loss is utilized to guide advanced target information to flow back to the fusion image generation module, so that the performance of the advanced vision task on the fusion image is effectively improved; according to the invention, self-adaptive dual-light fusion is carried out by combining respective advantages of infrared light and visible light, so that the method can adapt to more weather conditions and special user scenes, the target detection performance is improved, and the system safety is improved; the dual-light fusion technology can further optimize the detection rate and the false detection rate of target detection, the performance of a sensing module in various user scenes is improved, and the generated vehicle-mounted scene fusion image is more suitable for observation of a human concerned target.

The invention provides a remote sensing vehicle target detection method based on feature fusion and an attention mechanism. The method comprises the following steps: step 1, improving a YOLOv8 network to form a remote sensing vehicle target detection network; wherein the YOLOv8 network is improved, specifically, all C2f modules in a backbone network are replaced with focusing feature extraction modules (FFEM), and the FFEM modules are mainly obtained after a CA attention mechanism is fused into splicing operation in the C2f modules; 2, training the remote sensing vehicle target detection network to obtain a remote sensing vehicle target detection model; and step 3, inputting a to-be-detected remote sensing vehicle image into the remote sensing vehicle target detection model to obtain a detection result. According to the method, the small vehicle target can be well detected under the condition that the background of the remote sensing image is complex.