22results about How to "Improve target recognition accuracy" patented technology

The invention discloses a multi-source fusion detection and formation recognition system for an unmanned aerial vehicle cluster. The multi-source fusion detection and formation recognition system comprises a radar detection system, a photoelectric detection system and an information processing module. The invention further discloses a multi-source fusion detection and formation recognition method for the unmanned aerial vehicle cluster. The method comprises the steps that the radar detection system obtains the initial azimuth information of the unmanned aerial vehicle; the information processing module generates guidance information according to the initial azimuth information; the photoelectric detection system tracks the unmanned aerial vehicle according to the guidance information to obtain an image of the unmanned aerial vehicle; and the information processing module obtains the three-dimensional position information of the unmanned aerial vehicle according to the initial azimuth information and the unmanned aerial vehicle image, carries out the recognition of the unmanned aerial vehicle cluster formation pattern, and obtains a formation pattern structure. According to the method, accurate identification and three-dimensional coordinate acquisition of individual targets in a long-distance high-resolution unmanned aerial vehicle cluster and feature identification of the formation of the unmanned aerial vehicle can be realized, and high-reliability information support is provided for command, control and strike decisions.

The invention discloses a ship target identification method, device, equipment, medium and product, and relates to the field of artificial intelligence and synthetic aperture radar target identification, and the method comprises the steps: training an S3GAN network model according to an amplitude ship image in a source domain SAR ship image data set and SAR ship phase data in a target domain SAR ship image data set; s3, the GAN network model comprises a first generator network model for carrying out a task of converting from an amplitude domain to a frequency spectrum domain, a second generator network model for carrying out a task of converting from the frequency spectrum domain to the amplitude domain, and a discriminator; generating pseudo-SAR frequency spectrum information of an amplitude ship image to be converted based on the S3GAN network model; and inputting the pseudo SAR frequency spectrum information and the amplitude ship image into a feature fusion recognition network for feature fusion, and recognizing the SAR ship target in the fused image, so that the ship target recognition precision can be improved.

The invention discloses an HRRP sample generation method and system based on category guidance DDPM, and the method comprises the steps: carrying out the normalization and centroid alignment preprocessing of HRRP original data, and generating a small sample data set; a one-dimensional conditional convolutional network is constructed, the input of the one-dimensional conditional convolutional network is a to-be-denoised sample, the output of the one-dimensional conditional convolutional network is predicted noise of which the dimension is consistent with that of the input, and the one-dimensional conditional convolutional network comprises a down-sampling module and an up-sampling module integrated with a time step and an angular domain; training the one-dimensional conditional convolutional network by using the small sample data set to obtain a trained category guidance denoising diffusion probability model; and utilizing the trained category to guide the de-noising diffusion probability model to generate an expanded sample data set. The problems that in the field of HRRP sample generation, previous model training is unstable, the quality of generated samples is poor under the small sample condition, the angular domain of the generated samples of a conventional diffusion model is uncontrollable, and distribution outside samples are easily generated are solved.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle target classification matching strike control method and system based on deep learning, and the method comprises the following steps: S1, collecting the original environment data of an unmanned aerial vehicle flight region, carrying out the preprocessing of the original environment data, and generating multi-mode perception data; and S2, inputting the multi-modal sensing data into a pre-constructed deep learning classification network, extracting multi-level depth features of the target through the deep learning classification network, carrying out classification identification on the target based on the multi-level depth features, and outputting target category information and target position information of the target. The multi-modal sensing data is input into the deep learning classification network for target recognition, the multi-modal fusion sensing mode can give full play to the complementary advantages of different sensors, high target recognition accuracy can still be kept in complex environments such as night, low illumination and severe weather, and the adaptive capacity of the system to environment changes is remarkably improved.

The invention relates to the technical field of artificial intelligence and lip language recognition, and provides a flexible sensing device and a lip language recognition method based on the same. According to the flexible sensing device and the lip language recognition method based on the flexible sensing device, a data acquisition unit is used for acquiring differential electromyographic signals; the transmission cable is used for transmitting the collected differential electromyographic signals; the analog noise filtering module is used for twice amplification and low-pass and band-pass filtering; the acquisition mainboard is used for controlling analog-to-digital conversion, data preprocessing and wireless transmission; a data preprocessing module; the software denoising module is used for software denoising and normalization; and the upper computer is used for the feature extraction and screening unit, machine learning classification and visualization. According to the device and the method, the fixing and contact problems of the rigid electrode in the lip language muscle electric signal acquisition process are solved, and more effective denoising and more accurate human body lip language signal identification can be carried out.

The present application relates to the technical field of image processing, in particular to a fisheye lens image pixel-level edge enhancement and denoising method, comprising: a data acquisition step: acquiring original fisheye image data and preset fisheye lens optical distortion parameters; a weight generation step: determining the distortion stretching rate of a pixel point according to the optical distortion parameters; and generating a spatial density weight map; a threshold determination and denoising step: determining a target denoising threshold according to the spatial density weight map; denoising the original fisheye image data to generate an intermediate denoising image; a convolution kernel generation step: generating an adaptive curved surface convolution kernel according to the optical distortion parameters; an edge enhancement step: using the adaptive curved surface convolution kernel to perform edge enhancement processing on the intermediate denoising image; and generating an enhanced image; the present application effectively avoids the risk of irreversible erasure of key semantic features in the edge field, and ensures high-reliability feature preservation in the extreme field.

The application discloses a kind of soft membrane circuit board defect detection method and related device, the method includes: obtaining defect sample data set, the defect sample data set includes the circuit board sample image of multiple different defects;YOLOv7 target detection algorithm is used, part of data in the defect sample data set is used as training set, another part of data in the defect sample data set is used as verification set, and the training and verification of detection model are carried out;Get the detected circuit board image, and the template matching mode is used to accurately position each circuit part in the detected circuit board image, then the detection model is used for defect detection.According to the soft membrane circuit board defect detection method and device of the application, YOLOv7 target detection algorithm is used as basic detection model, is not sensitive to the change caused by transmission machine table shaking or unstable light source during image shooting, does not need to set a large number of processing parameters, and has higher target recognition accuracy.

The present invention provides a target recognition method, device and electronic system, which obtain an image to be processed containing a target to be recognized; input the image to be processed into a trained target recognition model to obtain a recognition result of the image to be processed; the target recognition model is trained in the following manner: determine the sample weight corresponding to each sample image in the sample image set; determine the loss value of the sample image based on the sample weight and loss function corresponding to the sample image, and train the initial model according to the loss value to obtain the target recognition model. In this method, different sample images correspond to different sample weights. During the training process of the target recognition model, the loss value corresponding to each sample image is determined for the sample weight and loss function corresponding to the sample image, and then the model parameters are adjusted based on the loss value. This training method is conducive to improving the overall performance of the target recognition model, thereby improving the target recognition accuracy of the model.

The invention discloses a bird and unmanned aerial vehicle identification method based on compensated multi-frame target slice and flight path fusion, and the method specifically comprises the steps: firstly, obtaining a multi-frame R-D domain slice data set of a rotor unmanned aerial vehicle and a bird through the staring distance compensation and Doppler compensation operation; secondly, a bidirectional GRU network model fusing track information is adopted, multi-frame target R-D domain slice data are input, and target feature extraction and integration are completed through double-layer convolution and a full connection layer; then, target track features are added into the integrated target feature set, and the target track features and the integrated target feature set are input into a bidirectional GRU network together; and finally, completing the output of a target identification result label through a full connection layer, and completing an identification task. According to the method, the track information and the multi-frame target information are combined, the track features and the motion features of the target are fully utilized, the target recognition accuracy is improved, and the method is simple, high in recognition speed and high in real-time performance.

The present application relates to the field of image processing and target image recognition technology of civil optical equipment such as intelligent traffic, city supervision and commercial monitoring, and particularly relates to a multi-target characteristic recognition method based on photoelectric image, which aims to improve the adaptability and accuracy of target recognition in target optical imaging, and the method obtains target motion image through photoelectric equipment, divides the target into relative static, straight line motion and turning state according to a preset threshold, extracts target motion trajectory, judges target state, respectively carries out straight line and arc line fitting on the target trajectory of straight line motion and turning state, analyzes the overall motion state of multiple targets in the imaging image based on the motion state trajectory, and adopts corresponding individual motion trajectory statistical analysis method for different states, the multi-state adaptive analysis capability of the method significantly improves the recognition accuracy, and the recognition accuracy of the target with frequently changing state in a complex scene is improved by more than 30%.

The present application relates to a kind of multi-modal visual large model under the dynamic target identification system of heterogeneous sensor anti-interference.The system includes: anti-background interference module, by modality convolution to multi-modal data of heterogeneous sensor, LSTM time series modeling and weighted fusion generation space-time feature, combine time / space attention mechanism and Transformer reinforced feature, use Gaussian probability density model to filter foreground pixel and extract foreground feature;Multi-modal processing module, separate each modality foreground feature and splice after refining, by Transformer extract consistent feature and complementary feature, after fusion and pooling enhancement generation reinforced fusion feature;Target identification module, fusion foreground feature and reinforced fusion feature, after convolution compression and Timesformer long time series modeling, input target detection head, output target classification and boundary box.The present application solves the problem of heterogeneous sensor data interference suppression and dynamic target accurate identification, improves robustness in complex scene.

The invention relates to a road target real-time detection and early warning system in a foggy environment. The system comprises a detection parameter determination module which is used for obtaining visibility parameters of a foggy environment and determining target detection parameters of sensing equipment based on the visibility parameters; the sensing equipment configuration module is used for configuring sensing equipment by adopting the target detection parameters and executing road target detection based on the configured sensing equipment to obtain a first target detection result; the target detection optimization module is used for performing detection optimization through a machine learning model based on the first target detection result to obtain a second target recognition result; the target attribute calculation module is used for calculating a target bounding box and a target motion trend based on the second target recognition result; and the risk early warning generation module is used for judging a target risk level according to the target bounding box and the target motion trend and generating a risk early warning signal. By adopting the system, the accuracy and the real-time performance of road target detection in a foggy environment can be improved.

The invention relates to the technical field of image processing, in particular to a fisheye lens image pixel-level edge enhancement and denoising method, which comprises the following steps: a data acquisition step: acquiring original fisheye image data and preset fisheye lens optical distortion parameters; a weight generation step: determining the distortion stretch rate of the pixel points according to the optical distortion parameters; generating a spatial density weight map; a threshold determination and denoising step: determining a target denoising threshold according to the spatial density weight map; de-noising the original fisheye image data to generate an intermediate de-noised image; a convolution kernel generation step: generating a self-adaptive curved surface convolution kernel according to the optical distortion parameters; an edge enhancement step of performing edge enhancement processing on the intermediate de-noised image by using an adaptive curved surface convolution kernel; generating an enhanced image; the risk that key semantic features of the edge view field are irreversibly erased is effectively avoided, and it is ensured that high-reliability feature retention is achieved in the extreme view field.

This invention relates to a method, apparatus, and computer-readable storage medium for underwater acoustic multi-target identification, belonging to the field of underwater target identification technology. The invention includes: calculating the detection features of an underwater acoustic audio signal, and inputting the detection feature vector into a first classifier for classification to determine whether a corresponding underwater acoustic target exists in the audio signal; if so, calculating the identification features of the audio signal and inputting the identification feature vector into a second classifier to determine the type of underwater acoustic target corresponding to the audio signal. This invention can directly perform feature calculation on the underwater acoustic audio signal, offering high real-time performance and fast response; and by analyzing the underwater acoustic audio signal from multiple angles to obtain detection and identification features, it reduces information loss during feature extraction; furthermore, it divides underwater acoustic target identification into two stages: detection and identification. First, the underwater acoustic target is detected based on the detection features, and then the underwater acoustic target type is identified based on the identification features, improving the accuracy of underwater acoustic target identification.

The invention discloses a moving target recognition model algorithm and system based on a Beidou smart map, and the model algorithm comprises the following steps: S1, positioning a moving target based on a pseudo-range positioning method in combination with a Kalman filtering method; the method comprises the following steps: 1, calculating satellite ascending node angular distance and position; 2, according to the category of the pseudo-range signal, judging whether noise eta j contained in the pseudo-range signal in a moving target pseudo-range positioning equation is added or not; s2, time calibration and distance correction are carried out on the latitude and longitude coordinate data after Kalman filtering processing, and the accuracy of target identification is improved; s3, unifying coordinates of the moving target and the Beidou satellite signal, and determining troposphere delay and ionosphere propagation delay; s4, establishing a moving target real-time observation equation according to the steps S1 to S3; the method has the beneficial effects that the non-line-of-sight deviation pseudo-range signal is introduced on the basis of Kalman filtering, and the target recognition precision is improved.

The invention discloses a driver attention distribution monitoring method based on double monocular cameras, and relates to the field of automatic driving safety, the method comprises the steps that two monocular cameras are arranged in a vehicle, the first camera monitors the driving behavior and the head posture of a driver, and the second camera is used for shooting the area watched by the driver in the driving process; carrying out region division on the image shot by the camera II, and numbering; using an Openface model to identify the number of the area watched by the driver in the step (3); obtaining a fixation point thermodynamic diagram of the AOI by using a ViT-Attention Rollout model pair; integrating the thermodynamic diagram, the piece data and the vehicle behavior data to carry out attention distribution analysis, and determining attention distribution modes of the driver in different driving environments and influence degrees of different events on attention distribution; according to the invention, related data acquisition is carried out through the two monocular cameras, so that interference caused by intrusive sight capturing equipment is eliminated.

The invention relates to the technical field of radio positioning detection, in particular to a self-adaptive multi-band radio wave identification method, which comprises the following steps of: monitoring data; judging a multi-target approaching state; judging a target shielding state; adjusting a frequency band to cope with interference; and correcting the threshold value and giving an alarm. According to the method, the target identification precision of the low-altitude aircraft can be effectively improved in a complex electromagnetic environment by combining the comprehensive analysis of the real-time reflection time delay, the reflection cross section and the interference ratio, the problem that a single frequency band is liable to be interfered is avoided by dynamically adjusting the frequency band, the identification capability of a shielded target is enhanced, and the identification accuracy of the low-altitude aircraft is improved. It is ensured that the targets can still be accurately detected in a multi-target approaching state and under the complex shielding condition, and the influence of an interference source on a recognition system can be effectively avoided based on continuous interference mode recognition and frequency band optimization of real-time data. The problems of poor real-time performance and low accuracy of signal identification caused by excessive signal processing staticity and calculation resource consumption are effectively solved.

A small sample SAR target recognition method based on a two-stage contrastive learning framework involves image recognition. Aiming at the practical problems of difficulty in obtaining SAR data sets and time-consuming and labor-intensive data annotation, a contrastive learning framework based on a two-stage training strategy is proposed to solve the SAR target recognition under small sample data conditions. The training process is decoupled into a representation learning stage and a classification learning stage, including: 1) In the representation learning stage, supervised contrastive learning is used to train the representation module of the network on the original SAR data set; 2) In the classification learning stage, the encoding module is fixed and the classification module of the network is trained using a rebalancing strategy. Based on contrastive learning in machine learning and combined with the idea of ​​decoupled learning, the method has strong practicality and good stability, and can meet the requirements of high standards for SAR target recognition accuracy in cases of imbalanced categories and small sample data.

The invention discloses an underwater target recognition method based on multi-dimensional time-frequency feature fusion, and relates to the technical field of underwater target recognition, in particular to the underwater target recognition method based on the multi-dimensional time-frequency feature fusion. The objective of the invention is to solve the problem of low recognition accuracy caused by feature redundancy and non-significant target category information in conventional underwater sound target recognition. The method comprises the following steps: step 1, constructing a training data set and a test data set based on an underwater target radiation noise database; 2, constructing a CAF-ViT model, wherein the CAF-ViT model sequentially comprises a feature extraction module, a feature fusion module and a target recognition module; the CAF-ViT model is trained on the basis of the training data set, and a trained CAF-ViT model is obtained; and step 3, predicting the data in the test data set based on the trained CAF-ViT model, and identifying the type of the underwater target.

The invention discloses an intelligent bag taking method and system based on target recognition analysis, and relates to the technical field of target recognition. The method comprises the following steps of bag taking interference analysis, bag taking identification analysis and bag taking decision feedback. According to the method, the bag taking interference analysis result is obtained based on the bag taking identification interference parameters so as to judge whether to carry out image dynamic acquisition adjustment or not, if yes, bag taking dynamic identification analysis is carried out after the image dynamic acquisition adjustment is carried out, and if not, bag taking dynamic identification analysis is directly carried out; recognition errors caused by ambient light changes or other factors are effectively avoided, recognition of the bag taking action is more accurate, whether a bag taking decision feedback mechanism is triggered or not is judged after bag taking dynamic recognition analysis is conducted, if yes, bag taking decision feedback is executed, and if not, image dynamic recognition optimization is executed, so that the bag taking action is more accurate. The reliability of the automatic bag taking machine in actual operation is effectively improved, and then recognition of the bag taking process of the automatic bag taking machine is more accurately carried out.

This application discloses a laser mapping method and apparatus, and a laser positioning method and apparatus. The laser mapping method includes: acquiring first image data and first laser point cloud data collected by a first vehicle in a preset operating area, and fusing the two data using a preset fusion algorithm to obtain a first fusion result; determining the target and its corresponding target type in the first laser point cloud data based on the first fusion result; preprocessing the target in the first laser point cloud data according to the target type using a preset preprocessing strategy to obtain preprocessed first laser point cloud data; and constructing a point cloud map of the preset operating area based on the preprocessed first laser point cloud data and corresponding post-processed positioning data. This application fuses image data and laser point cloud data for target recognition, improving target recognition accuracy. It also employs different preprocessing operations on the laser point cloud data for different target types, thus improving the accuracy of laser mapping and positioning.

Embodiments of the present application provide a target comprehensive identification method and device based on high-dimensional feature map, and equipment, the method comprises: obtaining observation data about target group based on radar sensor collection; processing the observation data, and performing feature extraction on the processing result to obtain multi-dimensional feature data about the target group; processing the multi-dimensional feature data to form a target multi-dimensional feature vector; inputting the target multi-dimensional feature vector into a self-encoder to realize data dimension reduction and deep feature extraction of the target multi-dimensional feature vector based on the self-encoder, and obtaining a target multi-dimensional feature map; inputting the target multi-dimensional feature map into a target neural network to classify the target multi-dimensional feature map based on the target neural network, and realizing target identification. The method based on the embodiments can effectively improve the accuracy and efficiency of target identification.