37results about How to "Reduce the false alarm rate of detection" patented technology

The invention discloses an adaptive fault detection method for an airplane rotation actuator driving device based on deep learning. According to the invention, adaptive fault detection is carried out on the airplane rotation actuator driving device based on a sparse Dropout automatic coder and a noise reduction automatic coder and deep learning of Logistic regression, feature self-learning of original data is realized through using the Dropout automatic coder in a first layer and a layered noise reduction automatic coder model in a second layer and a third layer by adopting a multi-layer neural network based deep learning autonomous cognitive method, data features acquired by learning are inputted to a Logistic regression model so as to judge an operating state of the rotation actuator driving device, a threshold is enabled to change along with different inputs and different states of the system through additionally arranging an adaptive threshold fault observer, and a residual error caused by non faults is eliminated. The method disclosed by the invention can be effectively applied to fault diagnosis of the airplane rotation actuator driving device.

The invention discloses a fault detection, diagnosis and performance evaluation method for a redundant aileron actuator. According to the method, fault detection, diagnosis, evaluation and real-time detection of the actuator are performed by means of an input order signal, an output displacement signal, a force motor current signal and aerodynamic loading data of the actuator; the fault detection is realized by a two-stage neural network, a first neural network is used as a system observer and is matched with actual output to acquire a residual error, and a second neural network outputs a self-adaptive threshold value synchronously; the fault detection is realized by the system observer and a force motor current observer; a time domain feature is extracted from a residual error signal and output to a self-organizing mapping neural network, and a minimum quantization error is acquired and normalized to a health degree, so that the actuator performance is evaluated; and on the basis of fault detection, the aerodynamic loading data is introduced, by means of a specific input order spectrum, the system observer and the neural network with the self-adaptive threshold value are trained, and the real-time fault detection is realized.

The invention discloses a water surface oil spill detection system and method based on ultraviolet images, and relates to the field of signal processing. Including sequentially connected ultraviolet imaging module, video decoding cache module, image processing module, alarm module; ultraviolet imaging module includes ultraviolet light source, ultraviolet bandpass filter and camera; video decoding cache module includes video decoding chip and synchronous dynamic random access memory , the camera is connected to the video decoding chip, and the video decoding chip is connected to the synchronous DRAM; the image processing module includes a DSP chip and a program loading chip, and the video decoding chip, the synchronous dynamic random access memory, and the program loading chip are all connected to the DSP chip; the alarm module includes The relay, the sound and light alarm and the wireless data transmitter, the DSP chip is connected with the sound and light alarm through the relay, and the DSP chip is connected with the wireless data transmitter through the network interface. The invention has good detection effect on water surface oil spill and strong anti-interference ability.

The invention diWscloses a method for detecting infrared small targets under a cloud background based on temperate filtering and false alarm rejection. The method comprises the following steps: firstly, obvious noises are removed from an image through maximum median filtering to complete image preprocessing; secondly, Robinson template filtering is used to suppress the background and highlight targets; thirdly, cloud partitioning of the original image is carried out, binarization processing of a result obtained after the Robinson template filtering is carried out through a low threshold in a cloud partition, and binarization processing of the result obtained after the Robinson template filtering is carried out through a high threshold in a non-cloud partition; finally, a plurality of ''false target points'' generated by a same target are further removed from the binarization results to complete ''coarse detection'', and time domain processing is continued for adjacent frame images that have gone through space domain processing to complete ''fine detection'', so that the detection of the infrared small targets is achieved. The method provided by the invention has the advantage that the constant false alarm rejection algorithm is added in interframe track correlation, so that the detection false alarm rate is greatly reduced.

The invention discloses an unmanned aerial vehicle video small-object detecting method based on super-resolution reconstruction. The unmanned aerial vehicle video small-object detecting method comprises the steps of selecting one input image as a reference frame, and selecting three consecutive image frames for performing sub-pixel displacement estimation on the reference frame; then placing the displacement estimation result of the four image frames into a high-resolution image grid; and estimating pixels which are lost in the high-resolution image grid, thereby obtaining an objective image with a relatively high resolution. Afterwards, an objective template is extracted from the objective image, and the characteristic of the objective image is calculated. Then the reconstructed objective image is divided for obtaining a plurality of objective area blocks. Characteristic extraction and characteristic identification are successively performed on all objective area blocks, thereby finishing preliminary detection for the object. Afterwards, false object elimination is performed, thereby obtaining a final detection result.

Embodiments of the present invention provide a method for detecting power transmission towers based on synthetic aperture radar images, by processing high-resolution synthetic aperture radar (Synthetic Aperture Radar, SAR) images to obtain a region of interest (Region of Interest, ROI), and Calculate the semi-variance feature of the corresponding area, and use the neural network to detect the transmission tower. The present invention does not require that the image must be an expensive full-polarization SAR image, and for the same SAR image, especially when the outline of the high-voltage transmission tower on the image is not clear and there are many false targets, the present invention can detect More transmission tower targets, and the detection false alarm rate is lower than the existing technology.

The invention provides a vehicle-mounted airport runway foreign object detection system, comprising: a vehicle, a vehicle-mounted millimeter-wave radar, a vehicle-mounted camera platform, video linkage monitoring equipment, and a display device; the vehicle-mounted millimeter-wave radar is used to detect foreign objects on the airport runway , and send an alarm message when a foreign object is found; the vehicle-mounted camera platform is used to obtain the original image of the foreign object according to the alarm information; the video linkage monitoring equipment is used to display the foreign object through the display device according to the original image. The invention also provides a corresponding method. The invention adopts a vehicle-mounted multi-sensor method to realize the detection of foreign objects on an airport runway, which can effectively guarantee the detection performance of the system. The system uses high-resolution imaging radar to detect foreign objects on the airport runway, effectively ensuring the system's detection performance in dark nights and rainy weather environments. The system adopts the radar detection and positioning video linkage monitoring method to effectively reduce the false alarm rate of detection and improve the operability of the removal of foreign objects on the airport runway.

The invention discloses a hyperspectral remote sensing small target detection method based on multiple aperture information processing. The hyperspectral remote sensing small target detection method based on the multiple aperture information processing comprises data observation, information processing and target detection. In the data observation, distributed parallel partitioned information processing performed on hyperspectral remote sensing data through a multiple aperture asynchronous mapping mechanism is simulated, partitioned data collection is performed on the hyperspectral remote sensing through a data collection chip, multiple aperture information obtaining through a fly visual system and a mapping mechanism are simulated, and the hyperspectral remote sensing spectrum data is decomposed and recombined according to an ommatidium mapping function to form into a spectrum data cube which is about local ground objects and construct a distributed parallel partitioned information processing mode. In the information processing, a cartridge system of the fly visual system is simulated to achieve integration of redundant compression and super sensitivity. In the object detection, a self-adaption mechanism of optic high order nerve cells of the fly visual system is simulated and an anomaly detection result is obtained through a self-adaption small target detection algorithm in combination with judgment of whole situation anomaly and local anomaly.

The invention discloses a graph-domain multi-scale infrared weak and small target detection method based on local gradient trilaterals. The method is as follows: converting an infrared image containing weak and small targets into a local gradient trilateral represented by a node and its edge weight relationship Graph signal; Secondly, according to the multi-scale transformation of the graph Laplacian matrix, the local gradient trilateral graph signal is multi-scale decomposed to obtain the low-frequency sub-bands of the graph signal at different scales; thirdly, according to the edge weight of the graph signal The relationship locally weights the high-frequency sub-bands of each scale, and takes the median value as the new central node coefficient, and then performs multiplicative fusion on the high-frequency sub-bands after local weighting; finally, the high-frequency sub-bands after multiplicative fusion The sub-band is segmented by adaptive threshold, the spatial position of the target is determined, and the detection result is output.

The invention provides a course error correcting method. The course error correcting method comprises the following steps: (a) collecting magnetic field measurement data, gyroscope measurement data and accelerometer measurement data; (b) calculating detection statistics; (c) determining whether a magnetic field is in a stable state or not in a corresponding time interval between an s step and a (s+m-1) step of a person; and (d) if the magnetic field is in the stable state, adopting a magnetic field solved course as an observation amount, estimating a course angle error by utilizing Kalman filtering, and correcting the course of the person by utilizing the course angle error. The invention also provides a course error correcting apparatus. The course error correcting apparatus comprises an accelerometer, a gyroscope, a magnetometer, a signal acquisition unit and a signal processing unit. The invention also provides a magnetic field detection method and apparatus.

The invention discloses a POLSAR image marine target detection method based on polarization direction angle compensation. According to the method, the polarization scattering property of an artificial metal marine target is used for detection, and in order to solve the problem that sea clutter reflection symmetry is affected by direction angle deviation generated by sea wave disturbance, direction angle compensation is conducted on a POLSAR image, and the detection accuracy of the marine target is improved. The method includes the steps that firstly, a completely-polarized image is acquired and related preprocessing operation is conducted; then, the polarized direction angle is calculated through a coherence matrix and direction angle compensation is conducted on the coherence matrix; finally, the intensity value of an element T'13 in the coherence matrix obtained after direction angle compensation is extracted and the artificial metal marine target is detected. The method overcomes the defects that a common marine target detection algorithm is complex in theory, difficult to achieve, poor in robustness and the like, and meanwhile starting from the polarization scattering property of the target, the marine target is accurately separated from sea clutters and target side lobes. The detection method is visual in principle, simple in algorithm and capable of facilitating programming realization and expansion.

The present invention provides a hyperspectral urban water body detection method based on adaptive sample selection. In the preprocessing of hyperspectral near-infrared band images, the noise band images are removed through the quality evaluation SSIM method, and the noise is further eliminated by using two average operations. , can obtain a more stable mean image of the near-infrared spectrum, compared with the traditional single-band threshold segmentation method, there is no need to manually select the image to be segmented; the invention uses the unsupervised threshold segmentation method to extract the suspected water body area, and then through the supervised Feature learning and classifier training, remove building shadows, building roof asphalt and other ground objects that are similar to water in the near-infrared spectrum from the suspected water body area; therefore, the present invention combines the unsupervised threshold segmentation method with supervised feature learning Combined with the classifier training method, it has the self-adaptive ability of the urban observation scene, and can realize the "real-time collection and real-time processing" of the measured scene data, and the false alarm is low.

An embodiment of the present invention provides a method and system for detecting a three-dimensional moving object, wherein the method includes: acquiring a three-dimensional range image of a multi-view scene to be processed, where the three-dimensional range image includes a three-dimensional moving target; extracting a three-dimensional distance of the multi-view scene The local invariant feature information of the image, and determine the feature description vector of the local invariant feature information; register the 3D range image of the multi-view scene according to the local invariant feature information and feature description vector; according to the registered multi-view scene The local invariant feature information of the 3D range image is used to determine the candidate detection area; the candidate detection area is verified and reviewed by iterative ground estimation and elevation filtering to obtain the accurate detection area; the 3D moving target detection is performed on the accurate detection area. The embodiment of the present invention reduces the detection false alarm rate, improves the detection efficiency, reduces the amount of data processing, and obtains a more accurate detection effect through repeated iterations.