14results about How to "Improve object detection performance" patented technology

The application provides a point cloud augmentation method and device, a computer readable storage medium and a terminal device. The point cloud augmentation method comprises: obtaining original point cloud data and at least one image, the original point cloud data and the at least one image being collected for the same target; projecting each original point cloud in the original point cloud data into the at least one image to obtain a plurality of projection points in the at least one image, each projection point corresponding to an original point cloud; selecting at least one adjacent pixel as an extended pixel in a vertical direction of each projection point according to a preset interval; and at least mapping each extended pixel to a three-dimensional space to obtain an augmented point cloud. The application can improve the number of point clouds and the effectiveness of the augmented point cloud.

The present application relates to the radar signal processing neighborhood, specifically to a kind of target detection method based on target space-time stability.The method includes: based on the time-domain signal representation of multiple frames sliding, generate subframe by dynamic matching filter, obtain subframe peak point information, including the energy, distance, doppler information of peak point on each subframe RD spectrum;Based on the spatial signal processing of single snap angle, generate multi-subframe peak point RDA spectrum, obtain the azimuth information of peak point on each subframe RD spectrum, utilize the feature information of each subframe peak point, remove false alarm point using false alarm suppression method based on mahalanobis distance;After false alarm suppression, the result is further removed discrete false alarm point using target track based on space-time stationarity, the method proposed in the present application improves the problem of too many false alarms in the RD spectrum after traditional clutter suppression algorithm;It can effectively reduce false alarm rate while retaining weak target signal, improve target detection capability;It is suitable for multiple system radars, and the application range is wide.

The embodiment of the application relates to an algorithm and a device of a Bayesian classifier of a non-uniform background, which are applied to an underwater active sonar system, the algorithm comprising: obtaining underwater data to be measured and auxiliary data through the active sonar system; the number K of the auxiliary data is greater than 0; modeling classification of the unknown covariance matrix structure into a binary hypothesis testing problem; hypotheses of the binary hypothesis testing problem comprise H i Wherein i=0, 1, H0 is a case that the unknown covariance matrix is a complex conjugate symmetric matrix; H1 is a case that the unknown covariance matrix is a real symmetric matrix; a Bayesian model is set, the Bayesian model comprising a complex inverse Wishart random matrix and a real inverse Wishart random matrix; a classifier for distinguishing the two hypotheses is obtained by using a minimum error probability criterion.

The present application relates to the field of active sonar target detection, and relates to a kind of adaptive target detection method and system suitable for reverberation edge environment.The method of the present application comprises: selecting the data of unit to be detected as main data, and selecting the received data of multiple distance units on both sides of the unit to be detected as overall auxiliary data;Overall auxiliary data is distributed in the three uniform reverberation areas formed by the left reverberation edge and the right reverberation edge of the unit to be detected, and is divided into three auxiliary data subsets accordingly;Modeling target adaptive detection as a binary hypothesis testing problem including no-target hypothesis and target hypothesis;Obtain the joint probability density function of main data and overall auxiliary data under no-target hypothesis and target hypothesis;Based on the modified two-step generalized likelihood ratio test criterion, the joint probability density function and the probability density function of main data and auxiliary data subsets are processed in turn, and the final detection expression is obtained to realize target adaptive detection.

This invention discloses a lightweight star catalog target detection method and system. The method includes: constructing a student model, which employs a lightweight feature extractor and a Transformer decoder with width and depth pruning; constructing a teacher model, which employs an un-lightweight feature extractor and a Transformer decoder without width and depth pruning; adjusting the parameters of the teacher and student models, calculating the value of the loss function, stopping the iteration when the value of the loss function is minimized, and obtaining trained teacher and student models; and using the trained student model to perform lightweight star catalog target detection. The advantages of this invention are: lightweight, low computational complexity, and the ability to achieve 3D position detection of star catalog targets.

The application relates to the field of small sample target detection, in particular to a two-stage small sample target detection method based on an optimized CBAM attention mechanism, and comprises the following steps: training a two-stage target detection network Faster-RCNN by using a base class data set to obtain a base class detection model; freezing parameters of a feature extraction backbone network in the base class detection model; optimizing a CBAM attention mechanism module; placing the optimized CBAM attention module in the feature extraction backbone network to construct a detection network, then inputting a new class small sample data set with a small amount of labeled information to fine-tune parameters of a detection head part of the detection network; and inputting a to-be-detected data set into the detection network to obtain a detection result. Compared with the prior art, the application has the advantages of inhibiting the influence of unimportant spatial information, improving the attention degree of important spatial information, enhancing the sensitivity to different scale features, and having strong generalization ability and robustness and the like.

This invention relates to the field of machine vision technology, specifically disclosing a multi-target tracking method with reference point prior position embedding and adaptive updating. It constructs a multi-target tracking network for multi-target tracking, within which a prior position generation network based on target queries is designed. This prior position generation network generates target queries with explicit prior position information. This prior position information facilitates effective keypoint sampling, solving the problem of lacking prior position information for target query reference points, thereby improving target detection performance. Furthermore, a tracking reference point prediction network based on tracking query reference points is designed. This tracking reference point prediction network generates new tracking query reference points for tracking queries, effectively reducing the deviation between the tracking reference point position and the target center position, achieving accurate positioning of the tracking reference point, and thus improving target tracking performance.

A small target detection method, system, device and medium based on a local attention feature correlation mechanism, the method comprising: data preprocessing, model construction, model training, small target detection; the system, device and medium are used to realize a small target detection method based on a local attention feature correlation mechanism; the application extracts spatial features and semantic features through a residual network, performs feature fusion through a feature pyramid mechanism, reconstructs features based on a local attention feature correlation mechanism, trains a model using a Hungarian matching algorithm, obtains an optimal matching model suitable for a data set, can ensure high accuracy with fewer iteration times, and has the characteristics of low network operation complexity, low training cost, easy model migration, and high model detection performance.

This invention relates to a dual-driven target detection method combining saliency attention and unsupervised image augmentation. The aim is to improve the accuracy and stability of target detection models in complex and degraded environments by introducing salient region information and a target detection feedback mechanism. This method falls under the fields of computer vision and deep learning. It constructs an unsupervised image augmentation network to enhance the input image and guides the network to focus on optimizing target-related regions in the image through a dual saliency attention mechanism. Simultaneously, the target detection model is embedded in the overall training process, and the detection loss provides inverse constraints and optimizations to the image augmentation process. By combining unsupervised image augmentation and target detection tasks, this invention achieves adaptive optimization of the augmentation results for the detection task, avoiding the problem of traditional augmentation methods focusing only on visual quality while neglecting downstream tasks. It does not rely on paired high-quality reference images, exhibits good generalization ability and robustness, and is suitable for target detection applications in complex underwater visual environments.

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.

The application discloses a kind of underwater acoustic signal's space-time stabilization method, system and storage medium, output space-time signal energy spectrum enters wideband stabilization module, judge its output as reverberation background part input to reverberation stabilization module, judge its output as noise background part input to narrowband stabilization module, output after processing is stabilized after space-time energy chart, finally through energy detection obtains the azimuth, distance and intensity information of target.Data verification is carried out by sea trial, the application can realize space-time signal stabilization in underwater long-distance target detection, solve the interference problem of reverberation, high-power white noise and colored noise, realize the effective detection of long-distance target echo.

The application is suitable for the field of radar technology, and provides a compensation method and device for echo power, a radar and a storage medium. The compensation method comprises the following steps: when a radar detects a target, determining a current echo power of a compensation device according to a distance between the radar and the compensation device, wherein the compensation device is located in a sensing range of the radar; subtracting the current echo power of the compensation device from a reference echo power of the compensation device to obtain a change value of the echo power of the compensation device in this time detection; and if the change value of the echo power of the compensation device in this time detection exceeds a preset range, compensating a current echo power of the radar according to the change value of the echo power of the compensation device in this time detection. Through the application, the interference of factors such as temperature drift, rain and snow attenuation, device aging and the like on the echo power of the radar can be reduced, and the target detection performance of the radar is improved.