95results about How to "Improve target recognition rate" patented technology

The invention discloses an image identification automobile live-action navigation device and method. The device comprises a video analysis module, an image identification module, a GPS (global positioning system) module, a route planning module, a map management module and a live-action navigation management module, wherein the video analysis module is connected with the image identification module, and the live-action navigation management module is respectively connected with the image identification module, the GPS module, the map management module and the route planning module. Automobile navigation equipment provides map display and route marking, images are identified according to images acquired by an automobile data recorder, particularly, traffic signs of variable lanes are identified, a user is reminded of selecting turning lanes or direct-through lanes when approaching an intersection and reminded of driving or parking according to actual traffic signal conditions, and live-action navigation is realized. The new function of automobile navigation is added, and user experience is enhanced.

The invention discloses a quick target identifying method and system based on compressed sensing. The method comprises steps of conducting spectrum dimensionality reduction via a spectrum selection method, sampling a target image and conducting spare transformation via a compressed sensing method, conducting compressed sensing reconstruction based on an orthogonal matching pursuit method, and identifying via an SVM classifier. Sampling and spare processing are conducted via the compressed sensing algorithm can reduce data post-calculation complexity, and can break through limitations of sampling frequency; spectrum selection and target identification algorithm are combined; with the high-spectral feature identification technology, unit target detection and multiple imaging detection can be combined; with the utilization of spectrum technology and imaging technology, the detection unit has a target identifying capacity, so target identifying rate can be improved; and target identifying time can be reduced and strong anti-jamming capability can be achieved.

The invention discloses a linkage control system for a small radar and a photoelectric turntable. The linkage control system comprises a small radar unit, a photoelectric turntable unit and a computer linkage control unit; the small radar unit and the photoelectric turntable unit are arranged at the front end; the computer linkage control unit is arranged at the rear end; the small radar unit comprises the small radar and a small radar data analysis module; the photoelectric turntable unit comprises the photoelectric turntable and a photoelectric turntable video acquisition module; a system control module, an image fusion processing module and multiple communication modules are arranged in the computer linkage control unit; the system control module is connected to an alarm module; and the image fusion processing module is connected to an electronic map. Compared with the prior art, synchronous display of detected targets on the electronic map can be carried out through linkage of the small radar, the photoelectric turntable and the electronic map; therefore, the target identification rate, the target display control and the early warning means of a security and protection system can be greatly improved.

The invention discloses a middle propeller cavitation noise numerical prediction characteristic extraction method based on inhomogeneous inflow. The method includes the following steps: first conducting mesh generation on a propeller computational domain and checking mesh quality and defining boundary conditions; then setting a computational model in computational fluid dynamics (CFD) software, conducting stable iterative computation to obtain water dripping performance parameters and inflow opening speed to verify the model accuracy; conducting non-stable iterative computation by utilizing stable computation as the initial value of the non-stable computation in the CFD software and displaying the cavitation period state of a propeller piece and cavitation area change of a recording piece through post processing; finally computing the propeller cavitation radiation noise through the cavitation area of the propeller piece according to a single-vacuole radiation noise theory to conduct characteristic extraction.

The invention provides a target monitoring method for an airport field monitoring system on the basis of video recognition. A video target is matched with a monitored target by a target recognition and location technology and a technology of converting projections of video coordinates and projections of coordinates of the field monitoring system, and the target recognition rate of the field monitoring system is improved.

The invention discloses an SAR image noise suppression method based on joint sparse representation and residual fusion and mainly solves the problems of insufficient speckle noise suppression and poor detail keeping of the conventional SAR image noise suppression method. The method disclosed by the invention comprises the following steps: (1), performing block-matching on images to obtain a similar set; (2), performing local variance estimation on the images; (3), performing joint sparse representation on the similar set through local variance and the WSOMP method, so as to obtain a sparse coefficient, and calculating a residual set; (4), performing residual fusion on the residual set, and performing noise reduction through the wavelet soft threshold algorithm to obtain the fused residual; (5), updating a dictionary through the fused residual and the sparse coefficient; (6), performing image reconstruction on the similar block set through the undated dictionary to obtain a de-noised block set; (7), returning the de-noised block set to the original positions of the images, so as to obtain de-noised images. The SAR image noise suppression method obviously improves the SAR image speckle noise reduction effect and can be used for SAR image target recognition and image enhancement.

The invention discloses an unmanned aerial vehicle multi-scale target detection and recognition method, which is high in recognition rate, small in calculation amount and high in robustness. The method is realized through the following technical scheme: forming a real-time target detection and identification network model by adopting three parts, namely a trunk structure for extracting different scale features, a network neck (Neck) and a network detection head for predicting target information, performing feature extraction on an input image by adopting a CSPDarknet53 trunk network in a target detection and identification network-YOLOv4, and expanding the original three-scale feature output into four-scale feature output; using an improved two-branch PANet to reduce the number of convolution layers through which features pass; predicting a conditional probability value for each category by each detection output, directly obtaining a prediction result from the picture, and obtaining target information; and transmitting the four feature maps with different sizes to a detection head for joint training, and performing category judgment and position regression on the unmanned aerial vehicle target to obtain a detection and recognition result.

The invention discloses a boat, ground and vehicle combined monitoring method. Searching load equipment on a bag body of a moored boat is used for conducting region searching, tracking and recognizing load equipment on an unmanned aerial vehicle is used for conducting close tracking and recognizing on a target searched by the load searching equipment, and recognition results are sent back to a ground monitoring station. The invention further discloses a boat, ground and vehicle combined monitoring system which comprises the moored boat, the unmanned aerial vehicle, ground equipment and the load equipment. The ground equipment comprises a multifunctional truck and the ground monitoring station. The load equipment comprises the searching load equipment and the tracking and recognizing load equipment. The moored boat carries the searching load equipment for achieving long-term hover and region searching; the unmanned aerial vehicle carries the tracking and recognizing load equipment for achieving rapid departure and close investigation; and the multifunctional truck enables the moored boat, the unmanned aerial vehicle and the load equipment to be integrated, rapid and mobile deployment is achieved, and the ground monitoring station is used for achieving information fusion and commanding and coordinating work, so that a combined monitoring function of the boat, ground and vehicle combined monitoring system is achieved.

The invention discloses a target identification method and a target identification system for an SAR (synthetic aperture radar). The method comprises the following steps that the complex image data of a tested SAR image is subjected to SCM (subaperture cross-correlation magnitude) target signal-to-noise ratio improvement processing; the processed image data is subjected to threshold division, and then, a target outline image of the tested SAR image is determined; after the Fourier description sub features are extracted on the target outline images, the feature vector of the tested SAR image and the feature vector of each training SAR image are obtained, and the similarity between the tested SAR image and each training SAR image is calculated; the target category of the training SAR image with the highest similarity to the tested SAR image is identified into the target category of the tested SAR image. The method and the system provided by the invention have the advantages that according to the technical scheme, the target identification rate is improved through improving the image data quality, and the identification speed is also accelerated, so the target identification performance is improved.

The invention discloses a SAR image target identification method based on a depth increment support vector machine. The method comprises steps of (1), inputting a SAR image; (2) pre-processing the image; (3) constructing an initial training set and an increment training set; (4), initializing the depth increment support vector machine; (5), calculating an initial identification rate of a test sample; (6), updating the depth increment support vector machine; and (7), calculating an identification rate of the test sample. According to the method, advantages of increment learning and depth learning are combined, the depth increment support vector machine is employed to carry out target identification of the SAR image, an increment training sample can be processed, and the depth target information with better discrimination property can be acquired. The method is advantaged in that SAR image target identification precision is improved, and the training time is shortened.

The invention provides a full Bayes features extraction method for synthesizing an aperture radar object, which can adaptively estimate the quantity of the attribute scattering centers and parameters of the attribute scattering centers. The full Bayes feature extraction method for synthesizing aperture radar object comprises steps of receiving a attribute scattering center model of a radar echo signal, determining distribution of posterior probability that each unknown characteristic parameters is subordinated to, establishing a whole layered Bayes model, determining unite posteriori possibility distribution of all unknown parameters in the attribute scattering center model, determining suggestion distribution of each unknown parameter, performing sampling on each unknown characteristic parameter, obtaining N sampling values of each unknown characteristic parameter, generating a Markov chain of the unknown characteristic parameter according to the N sampling values of each unknown characteristic parameters, and determining an estimation value of each unknown characteristic parameter.

The invention provides an improved One-Class SVM (Support Vector Machine) algorithm based multi-mode composite detection target identifying method. The method is carried out through the following steps: on the pretreatment of the signals outputted from a passive millimeter wave detector, a long infrared detector and an FMCW radar in steadily scanning state, selecting the features with high distinguishing degree as classification features; using the improved One-Class SVM algorithm based algorithm to train the training data; constructing a classifier with a high target identification rate; extracting the features of a to-be-tested signal; sending the processed feature data to the classifier for target identification; and obtaining the determination result afterwards. According to the invention, the method considers the rapid development of the existing high speed real time signal processing system and the large amount of information to be processed in composite detection, and starting from the perspective of feature layer integration, uses a One-Class SVM algorithm featuring the sample and feature weights, which greatly improves the target identification rate of a multi-mode composite detector.

The invention provides a radar target recognition method based on a dpKMMDP model, and is used for solving the technical problem of low recognition rate of the existing radar target recognition method based on a classifier model. The realization steps are that 1. a radar SAR image training sample set X is extracted; 2. the kernel function matrix G of the training sample set is calculated; 3. the dpKMMDP model is constructed and joint condition posterior distribution of all the parameters of the model is calculated; 4. condition posterior distribution of all the parameters is calculated; 5. the initial value of each parameter is set; 6. cyclic sampling of all the parameters is performed, and the sampling result of the parameters required by the T0 time test phase is saved; 7. a test sample set X<^> is extracted and the hidden variable set Z<^><n> of the test sample set X<^> is calculated; and 8. the hidden variable set Z<^><n> is substituted in an LVSVM classifier to calculate and output the target category number of the test samples. Classification of complex nonlinear classifiable radar SAR image data can be realized, and the method is used for recognition of radar targets.

The invention discloses a fixed area night fog all-weather color video imaging system and a building method thereof. The system comprises a visible light video collection system, a visible light image collection module, a background image updating module, a visible light background image bank, an infrared light video collection system, an infrared light image collection module, an image preprocessing module, an infrared light background image bank, an infrared light target detection module, an image registration module, a video fusion module, a video signal emitting module, an RTSP transmission module, and a display module. The system uses an improved algorithm integrating a mixed Gaussian model and a symmetric difference method as well as an imaging concept of fusion of the infrared video and visible light background image. After processing by a data processing center, the system can output a clear color video image at night, in fog weather or other weather with poor visibility, so as to realize the color effect of the infrared light video truly. The system is easy to realize, is low in cost, is easy to install, and realizes long-distance real-time monitoring.

The invention discloses a radar target recognition method based on noise backgrounds of plural AGC models. The radar target recognition method mainly solves the problems that high recognition rate and small amount of computation can not be achieved at the same time when target recognition is carried out in a noise background in the prior art. The radar target recognition method includes the steps of using radar repeated distance image training samples to calculate variance images of the radar repeated distance image training samples, calculating signal to noise ratio of radar repeated distance image testing samples, correcting the variance images of the repeated distance image training samples according to the signal to noise ratio, and calculating decision values of posteriori probabilities of lower classes of each category according to the corrected variance images and the repeated distance image testing samples to determine target category labels. The radar target recognition method has the advantages of being high in recognition rate and small in amount of computation under the noise backgrounds, and being applicable to radar target recognition.

An embodiment of the invention discloses a method and device for adjusting illumination balance. The method comprises the follow steps: carrying out region division on an object region image based on statistical characteristics of a color histogram, determining a classifier corresponding to each region according to correspondence relation between the statistical characteristics and classifiers, and obtaining a color histogram of the object region image according to correspondence relation between the classifiers and histograms; carrying out comparison to judge whether a distribution equilibrium value of the color histogram of the object region image is within the range of a distribution equilibrium value of a color histogram of a sample image; and if not, adjusting light balance parameters of the object region image to enable the distribution equilibrium value of the color histogram of the object region image to be within the range of the distribution equilibrium value of the color histogram of the sample image. The method and device for adjusting illumination balance can solve the technical problem of illumination imbalance in the prior art, thereby improving object recognition rate.

The invention provides a stable imaging method applicable to two-dimensional wide-area scanning. The core of the method is geographical stabilization and electron image movement compensation matching, forward movement of a photoelectric system is compensated in a geographical stabilization manner, the movement of the azimuth is compensated in a geographical stabilization and electron image movement combined manner, and clear imaging of the photoelectric system in the two-dimensional wide-area scanning process is finally realized. The method is mainly realized through algorithms, the structural form is simple, the imaging quality of the photoelectric system can be effectively improved, and the reliability of the photoelectric system can be improved.

Provided is an object identification method based on high-resolution one-dimensional image information fusion. The purpose of the invention is to solve how to perform information fusion on the observations of different radars in a network in order to increase resolution. The method comprises a step 1: detecting two aircrafts by using two kinds of radars with different waveforms generated by BSS so as to obtain echo data of the two different radars; a step 2 of performing pulse compression on the echo data to obtain two groups of high-resolution one-dimensional images of an object; a step 3 of performing data fusion on the two groups of high-resolution one-dimensional images by using a weighted average method to obtain a new group of one-dimensional images, wherein a fusion weight is randomly selected between 0 to 1; a step 4 of subjecting the two groups of high-resolution one-dimensional images to a set threshold to acquire the number of sampling points; a step 5 of multiplying the number of sampling points of the new high-resolution one-dimensional images by a preset distance resolution (actual distance between the two sampling points) to obtain the length of the object; and a step 6 of performing object identification by using Bayesian classification.