71results about How to "Improve target recognition accuracy" patented technology

The invention, which belongs to the technical field of multi-sensor information fusion, discloses an evidence-synthesis-based information-fusion target recognition method. A plurality of sensors are used for carrying out attribute information collection on a to-be-identified target and a feature attribute is extracted from the collected attribute information; data having the feature attribute aredivided into training data and testing data, wherein the training data are used for constructing a neural network model and the testing data are used for obtaining a basic probability assignment value; and then evidences are synthesized based on an improved evidence synthesis method and the synthesized result is used as the target recognition basis. According to the invention, the basic probability assignment values of evidences are obtained accurately and a synthesis problem of high-conflict evidences is solved. The basic probability assignment values of evidences are obtained by using the neural network and the neural network has the high nonlinear mapping capability and is capable of mapping the intrinsic relationship between the target feature data, so that the accuracy of the basic probability assignment values is ensured, the conformance to the real scene is realized, and the practical significance is good.

The invention discloses a DHOG and discrete cosine transform-based overhead line system equilibrium line fault detection method. The method includes an equilibrium line and equilibrium line supporting seat recognition and positioning process and an equilibrium line default fault detection process. The equilibrium line and equilibrium line supporting seat recognition and positioning process includes the following steps that: DHOG feature extraction is performed on a positive sample and a negative sample; and an AdaBoost classifier is trained; and a classifier obtained through training is utilized to accurately judge whether a target area is contained in a slide detection window. The equilibrium line default fault detection process includes the following steps that: the target feature information of the equilibrium line and a supporting seat thereof is enhanced through utilizing discrete cosine transform; a two-step method which performs circle detection based on arc piecewise fitting is adopted to perform statistics on circles in an image; and an overhead line system equilibrium line default fault can be judged according the number of the circles. The DHOG and discrete cosine transform-based overhead line system equilibrium line fault detection method has the advantages of high detection accuracy and excellent robustness under a situation in which rotation and scale variation occur on the target image, and can provide a certain technical reference for intelligent inspection of high-speed rail overhead line systems.

The invention belongs to the technical field of video processing, and specifically relates to a cross-camera video concentration method based on pedestrian re-identification. The specific steps are asfollows: (1) using multiple segments of obtained videos shot by different cameras as input, and finally outputting multiple segments of concentrated videos; and (2) based on the multiple segments ofconcentrated videos obtained in a video concentration phase, in view of a specific target object, firstly searching for specific locations of the target object appearing in different videos in the concentrated videos according to a corresponding matching measurement method, and extracting concentrated video segments containing the target object to obtain the description of a coherent motion trajectory of the target object in the multiple videos. By adoption of the method of the invention, a lot of manpower can not only be saved, and the identification accuracy of the target object is also improved to some extent. The method proposed by the invention has an important value for practical application.

The invention provides a method for identifying a space target, which comprises the following steps of: performing envelope alignment preprocessing on HRRP sample data, and eliminating redundancy of the HRRP sample data subjected to the envelope alignment preprocessing; performing envelope amplitude preprocessing on the HRRP sample data after redundancy elimination, and forming a training set by the data set and a tag set corresponding to the data set; Setting network parameters, and constructing a GRU neural network; using the training set to train the GRU neural network, and reserving a partof data of the training set for verification to obtain a trained GRU neural network model; And preprocessing the HRRP data of the space target to be identified, inputting the preprocessed HRRP data into the GRU neural network model, and taking the highest probability as a space target identification result. According to the method, the deep essential characteristics of the radar HRRP data can beautomatically extracted by constructing an RNN neural network, so that the identification of a space target is completed. The influence of manual feature selection on the recognition result is eliminated, the time and energy of feature extraction are greatly reduced, and the recognition precision of the space target is improved.

The invention discloses a multiband image fusion recognition method. According to the method, a medium-wave infrared image and a long-wave infrared image are fused and used to supplement a training sample; and data processing is performed on the basis of the supplemented training sample. In this way, classified division can be performed on a visible light image with abundant details and color information and a high recognition by use of respective advantages of the medium-wave infrared image and the long-wave infrared image, data with respective maximum advantages is fused, and therefore information loss can be lowered to the maximum. Through the method, three types of classifiers are constructed first, and a fusion image of the medium-wave infrared image and the long-wave infrared image serves as the supplemented training sample; and since a feature early fusion technology is applied to the fusion image, the original single training sample has the outstanding characteristics of respective advantages of the medium-wave infrared image and the long-wave infrared image, and the probability of respective advantage information of medium-wave infrared and long-wave infrared is increased.

The invention belongs to the technical field of biological radar, and discloses a multi-human-body target recognition and positioning method based on multi-base IR-UWB biological radar signals. The method is based on a one-transmitting three-receiving three-channel biological radar. Echo signals are subjected to preprocessing and inflection point extraction, secondary inflection point signals of three channels and the first three largest values of the secondary inflection point signals are obtained, the peak-background ratio and the correlation coefficient mean value of the first three largestvalues are calculated, and recognition and positioning of multiple human body targets are achieved through judgment of the peak-background ratio and the correlation coefficient mean value of the three largest values. According to signal characteristics of human respiration and the like, a set of target discrimination program combining two characteristic parameters of an energy-to-noise ratio anda correlation coefficient mean value is adopted, recognition and distance direction positioning of multiple human targets are realized, and the target recognition accuracy is improved.

The invention relates to a novel submarine cable water area AIS target detection method. The method comprises the steps that AIS data sent by a ship around a harbor area is received, the water area ofthe harbor area is subjected to hierarchical gridding, and a ship ID is associated with coordinate positions of different levels; the AIS data is screened according to a time interval and a selectedtarget area, grid processing is carried out on the target area, and the frequency of the ship at different levels of grids is calculated to obtain a ship distribution frequency histogram; an image model is built by utilizing ship position information, the ship sailing trend is predicted by using a discrete random distribution Markov prediction model, and a prediction curve of the navigation trackof the target ship is formed; the information of the ship near the high-grade warning water area is extracted, and the information of the ship is identified in a graded grid chart of the harbor area,wherein the information of the ship includes the ship ID, the course and the navigational speed; a VHF radio station is controlled to broadcast to the ship near the high-grade warning water area. According to the method, the safety of the submarine cable water area is improved.

The invention relates to an SAR image target recognition method based on a multi-discriminator generative adversarial network, and the method comprises the steps: obtaining an original real sample, and generating a training sample data set based on a multi-discriminator generative adversarial network model; training the convolutional neural network by using the training sample data set to obtain atrained target recognition model; and inputting a to-be-detected SAR image to the target recognition model, and outputting a target recognition result corresponding to the to-be-detected SAR image through feature extraction and feature matching. Compared with the prior art, the invention provides a multi-discriminator generative adversarial network model for generating a high-quality and high-stability training sample data set. A training mode of multi-discriminator joint feedback is adopted, a dynamic adjustment selection function is utilized to fuse output results of discriminators to complete update training of generators, training stability and generated sample quality are ensured, the reliability of subsequent target recognition model training is further improved, and accuracy of SARimage target recognition is improved.

The invention discloses a method for making electroencephalogram target judgment with assistance of machine vision. With the method, a problem that the single-trial P300 detection algorithm precision is reduced because of changing of a P300 component incubation period due to specific implication of a target image can be solved. The method comprises: (1), an electroencephalogram signal collection device collects a tested electroencephalogram signal; (2), a changing error of a tested P300 component incubation period is determined; (3), a position of a target image in an image sequence is localized based on the P300 component; (4), a target candidate image is determined by combining the incubation period changing error determined at the step (2) and the target image position determined by the single-trail detection algorithm at the step (3); and (5), identification and classification are carried out on the target candidate image by using machine vision, and an image type with the highest occurrence frequency is used as a concerned target image type on trial. The method has advantages of high accuracy and low false alarm rate.

The invention provides a target identification method and device based on laser scanning, and the method comprises the steps: obtaining the data of each point obtained through the laser scanning reflection of a to-be-identified scene through a laser radar, and calculating the three-dimensional coordinate value of each point according to the data of each point, wherein the data comprises a linear distance value between a sampling point scanned by the laser radar and the laser radar; obtaining a two-dimensional coordinate value of each point according to the three-dimensional coordinate value ofeach point, and clustering each point according to the linear distance value of each point and the two-dimensional coordinate value of each point to generate a plurality of categories; calculating each piece of target information according to the two-dimensional coordinate values of each category of points formed by clustering; and determining each target in the to-be-identified scene according to each piece of target information. In order to solve the problem that the horizontal resolution and the vertical resolution of the vehicle-mounted laser radar are different, the density clustering algorithm is adopted for clustering, the clustering radius is self-adaption about the linear distance, the obtained clustering result is good, and therefore the target identification precision is improved.

The invention relates to an early warning method and apparatus based on video analysis, an electronic device and a storage medium. The method comprises the steps of: performing target detection on a captured video stream of a preset geographic area, and determining a first image frame comprising a target in the video stream; when the state of the video stream is an early warning silence state, andthe first image frame is detected for multiple times within a preset first time length, changing the state of the video stream into an early warning activation state, wherein the early warning silence state is used for indicating that the target does not exist in the preset geographic area, and the early warning activation state is used for indicating that the target exists in the preset geographic area; and in the case that the first image frame is detected for multiple times within a preset second time length after the video stream enters the early warning activation state, sending early warning information for the video stream. By adopting the early warning method and apparatus provided by the embodiment of the invention, the target recognition accuracy can be improved, reasonable early warning is effectively realized, and the trouble caused by excessive early warning to users is reduced.

The invention provides an SAR target identification method combining few-sample learning and target attribute characteristics. The SAR target identification method comprises the following steps: acquiring a source domain data set R, a target domain data set E, a target domain support set ES, a target domain query set EQ and a target attribute characteristic set A of R and ES; constructing a visualclassification network F1; performing iterative training on the visual classification network F1 by using R; constructing a visual attribute classification network F; performing iterative training onthe visual attribute classification network F by using the source domain data set R, the target domain support set ES and the target attribute feature set A thereof; and obtaining a target recognition result of the SAR image by using the target domain support set ES, the target domain query set EQ and the target attribute feature set A'o thereof. According to the SAR target identification method,the SAR target recognition performance under the condition that training samples of known categories are limited is improved by combining few-sample learning and target attribute characteristics.

The invention relates to a training method and application of a head and shoulder image classifier, and the method comprises the steps: obtaining a human body posture data set which comprises a plurality of human body sample pictures and human body head and shoulder labeling data corresponding to each sample picture; Synchronously obtaining an aggregation channel feature map and a downsampling aggregation channel feature map corresponding to each human body sample picture through a parallel algorithm; Based on the feature map, determining a positive detection window group and a corresponding positive feature vector group, and a negative detection window group and a corresponding negative feature vector group; And on the basis of the positive feature vector group and the negative feature vector group, performing multi-stage training on the enhanced decision tree to obtain a head and shoulder image classifier. Through a parallel algorithm, the head and shoulder detection speed is improved, and head and shoulder detection can be carried out in real time in a video stream; A feature map containing a plurality of feature channels is used for training, and a classifier result obtained through training is more reliable; And multi-stage training is carried out on the enhanced decision tree, so that the identification precision of the head and shoulder image classifier is greatly improved.

The invention provides an integrated imaging identification system and method based on a dynamic vision sensor, and aims to solve the technical problems of high system power consumption and low targetidentification accuracy in the prior art. The imaging identification method comprises the following steps: a lens collects a plurality of images; the dynamic vision sensor senses the change of the image gray scale; a processor in the data acquisition module analyzes the signals output by the dynamic vision sensor to obtain an address event data stream; a denoising sub-module in the target recognition module is used for denoising the address event data flow; a segmentation sub-module in the target identification module segments the address event data stream; an identification sub-module in thetarget identification module identifies the address event data flow; and the data visualization module acquires an imaging identification result.

The invention discloses an aqueduct personnel identification system, an identification method thereof and an aqueduct personnel identification system, the system comprises an acquisition front end, anexchanger and a signal emitter, the acquisition front end is in communication connection with the exchanger, and the signal emitter is also in communication connection with the exchanger; The exchanger is also connected with an external network; The aqueduct scene monitoring system is characterized in that the acquisition front end acquires a monitoring picture of an aqueduct scene, the system further comprises a video analyzer, the video analyzer is built by a deep learning method, and the video analyzer is in communication connection with the exchanger. In the aqueduct scene, monitoring images shot by the collection front end suffer from the conditions of small targets, fuzzy characteristics, high personnel mobility and more interference factors such as vehicles, ships and umbrellas inevitably, the method is used for personnel detection, interference of small targets and negative samples except people can be effectively identified, the identification speed is high, and the system identification accuracy is high.