705results about How to "Reduce missed detection" patented technology

The invention discloses a prostate puncture biopsy surgical robot which is used for helping a doctor to do a prostate puncture biopsy surgery. The prostate puncture biopsy surgical robot comprises a hoisting adjusting mechanism, a first linear movement mechanism, a second linear movement mechanism, a first angle adjustment mechanism, a second angle adjustment mechanism and a puncture assembly. A puncture angle is only determined through the first angle adjustment mechanism and the second angle adjustment mechanism; alignment of a puncture part is jointly completely by the hoisting adjusting mechanism, the first linear movement mechanism and the second linear movement mechanism; and the puncture assembly is used for controlling the feeding of a puncture needle. According to the prostate puncture biopsy surgical robot, position adjustment and angle adjustment are independent from each other and do not influence each other, so that the operation flexibility of the surgery is high; an adjusting range can be enlarged as much as possible through the control of six degrees of freedom under the condition that a surgery position and the surgery angle are accurate, so that the prostate puncture biopsy surgical robot is adaptive to actual requirements of different environments and different patients; and the position and angle are flexible to adjust, a puncture process is stable and the precision of puncture biopsy is improved.

The invention discloses a saliency-based synthetic aperture radar (SAR) image airfield runway edge detection method, which comprises the following implementation steps: (1) reading in image data; (2) carrying out median filtering on the image data which is read in; (3) carrying out enhanced processing on a filtered and de-noised image; (4) obtaining a salient map of the processed image; (5) rejecting a noisy background area on the basis of the saliency information of the salient map; (6) obtaining a seed growth point to carry out region growing; (7) carrying out aftertreatment; and (8) carrying out edge positioning. According to the saliency-based SAR image airfield runway edge detection method, the airfield runway edge can be quickly detected, the airfield runway edge information can be favorably kept, meanwhile, the interference of the noisy background area is avoided, and the saliency-based SAR image airfield runway edge detection method has better instantaneity and higher detection precision and can be applied to the SAR image airfield runway edge detection.

The invention discloses a face detection method and device adopting a cascade classifier, relating to the field of video monitoring. The face detection method comprises the following step of: carrying out face detection on rectangular windows with different positions and sizes in a current image to be detected by adopting a light type cascade-structure classifier to detect face windows, wherein each level of the light type cascade-structure classifier is a weak classifier. For a large quantity of windows to be detected in one image, relative to a scheme that a cascade-structure classifier is bound to eliminate partial non-faces after computing the results of all weak classifiers of a strong classifier, each weak classifier of the light type cascade-structure classifier can eliminate the partial non-faces, and therefore faster detection speed can be achieved, and the real-time detection of faces is further realized on megapixel high-definition videos.

The invention provides a statistical method and a statistical device for incremental coverage information. The statistical method comprises the following steps: S1, acquiring a test version code and a baseline version code; S2, running all single test cases for the test version code, acquiring coverage information of all functions in the test version code, and comparing the test version code and the baseline version code so as to obtain newly-added or modified function information; and S3, acquiring newly-added or modified incremental coverage information from the coverage information of all the functions according to the newly-added or modified function information. Compared with the prior art, the statistical method and the statistical device can automatically count the incremental information of a test version, so that a tester can quickly acquire the incremental coverage information and pertinently design a test case according to the incremental coverage information, thus improving the test efficiency and the test accuracy and reducing the missing test.

The invention discloses an ultrasonic inspecting and scanning device of a welding line, comprising a main frame, an idler wheel mechanism and a scanning mechanism, wherein the idler wheel mechanism is connected with the main frame, the scanning mechanism is arranged on the main frame and comprises a slanting scanning mechanism, and the slanting scanning mechanism is provided with a slanting scanning arm which is configured aslant relatively to a welding line to be inspected. The main frame is rectangular, and four edges of the main frame are all provided with guide rails; the slanting scanning mechanism comprises two slanting scanning brackets, a slanting guide rail and two slanting scanning arms, wherein the slanting guide rail is connected between two slanting scanning brackets, and the slanting scanning arms are arranged on the slanting guide rails; one slanting scanning bracket is straddled on two guide rails of two adjacent edges of the main frame, and the other slanting scanning bracket is straddled on two guide rails of other two adjacent edges. The ultrasonic inspecting and scanning device of a welding line, which is provided by the invention, can effectively solve the problem of missed detection of horizontal defects, enlarge defect detection range, realize overall scanning of the welding line and reduce potential safety hazard.

The invention discloses an edge detection method based on a gray-scale image of a specific class. The method is improved on the basis of a traditional canny edge detection algorithm; therefore, sensitivity to Gaussian noise and salt and pepper noise can be effectively reduced, adaptivity to different images can be strengthened, and edge detection precision and accuracy can be improved. The method comprises the following steps that: Gaussian filtering and median filtering are employed to remove Gaussian noise and salt and pepper noise in an image; image enhancement is carried out on the filtered image; non-maxima suppression is carried out on the enhanced image as well as description and analyzing are carried out in a gradient histogram mode; adaptive setting on a high threshold value and a low threshold value of a detection and connection edge is realized; and an edge tracking algorithm is set to realize connection of edge points. According to the edge detection method provided in the invention, edge extraction under circumstances that Gaussian noise and salt and pepper noise interferences exist can be effectively improved; adaptivity to different images can be improved; and good edge detection precision and accuracy can be realized.

The invention discloses a fluorescent PCR (Polymerase Chain Reaction) detection kit for African swine fever virus (ASFV). The fluorescent PCR detection kit comprises an ASFV-reaction solution, a DNA (Deoxyribonucleic Acid) enzyme mixed solution, an ASFV-internal standard, an ASFV-positive control, an ASFV-negative control, a nucleic acid releasing reagent and an ASFV-quantitative reference material, wherein the ASFV-reaction solution is prepared from primers: ASF03F03 and ASF03R03; internal standard primers: IPC05F01 and IPC05R01; a probe ASF03P and an internal standard probe IPC05P; the primers and the probes correspond to highly conserved fragments of an African swine fever virus p72 gene. The invention further discloses a method for preparing the fluorescent PCR detection kit for the African swine fever virus and a detection method utilizing the fluorescent PCR detection kit.

The invention discloses a traffic signal identification method and device, vehicle navigation equipment and an unmanned vehicle. The method comprises the steps of obtaining a current image in a videoshot by the unmanned vehicle; when the image detection condition is satisfied, detecting a region of interest of a traffic signal lamp in the current image through a traffic signal lamp detection model; otherwise, based on the region of interest of the traffic signal lamp in the previous frame of image of the current image in the video, determining the region of interest of the traffic signal lampin the current image by using the target tracking model; using a trained traffic signal lamp state identification model; identifying a traffic signal represented by a traffic signal lamp in a regionof interest of the current image; in view of the fact that the calculated amount of the target tracking algorithm is smaller than the calculated amount of the model detection. The region of interest of the traffic signal lamps in the continuous frames are identified in combination with the model detection and the target tracking, recognition of each frame of image can be achieved, the possibilityof missing detection is reduced, and the calculation cost for recognizing the continuous frames can be effectively reduced.

The invention provides a pedestrian detection method based on a time-domain convolutional neural network. The pedestrian detection method of a single image is expanded for successive frames of images, the convolutional neural network is trained to learn the spatial correlation of the single video image and the time correlation between the successive frames, and the defect of detection of shielded pedestrians by the conventional method is overcome. Besides, pedestrian detection is conducted by employing the time-domain convolutional neural network so that the posture change for pedestrians is more robust, and the overall detection accuracy and the recall rate are both improved.

The invention provides a news content sensitive word filtering method and system. The method comprises the steps of S1, preprocessing obtained news texts; S2, filtering the sensitive words of the news texts by employing a sensitive word multi-level filtering algorithm on the basis of the priorities of the sensitive words according to a pre-established sensitive word library; S3, judging the filtered sensitive words through an emotion analysis model based on a markov logic network when there are preset sensitive words in the news texts; and S4, marking the news texts as negative news when it is judged that the filtered sensitive words are bad sensitive words, otherwise, marking the news texts as positive news. According to the method and the system, secondary judgment is carried out on the filtered sensitive words through establishment of the emotion analysis model based on the markov logic network, thereby determining whether the filtered sensitive words have negative information or not; therefore, the negative news is filtered; moreover, the positive news fighting against the negative information will not be filtered; and the reliability of filtering the news content sensitive words can be improved.

The invention is applied in the field of test technology and provides a method and a system for controlling a power supply test and a power supply test method. The method comprises the following steps of: reading preconfigured power supply test process information which comprises power supply test standard parameters and power supply test item information; according to the power supply test item information, sending a power supply test item testing command to power supply test auxiliary equipment and controlling the power supply test auxiliary equipment to test all parameters of a power supply to be tested; receiving test result parameters fed back by the power supply test auxiliary equipment; comparing the test result parameters which are fed back by the power supply test auxiliary equipment with the power supply test standard parameters and judging whether power supply test items pass the test or not; and when the power supply test items pass the test, generating and storing a test report. The method and the system for controlling the power supply test and the power supply test method perfect the conventional power supply test process, reduce the test leakage and the wrong judgment and improve the test quality and efficiency.

The invention relates to a millimeter wave radar and vision cooperative target detection and identification method based on a neural network. A millimeter wave radar can obtain information such as theposition and the speed of an object by transmitting and receiving millimeter waves. A camera can obtain a visual image of an object. The position information obtained by the millimeter wave radar ismapped to the image. The region of interest is delineated in the image feature map. The region of interest is sent to the deep learning neural network to be processed, and the identification and positioning information of the target can be obtained. According to the invention, a millimeter wave radar and visual processing combined mode is adopted to complete target detection and recognition tasks.By means of a deep learning neural network technology, the object positioning time is shortened, and the recognition accuracy is improved.

An embodiment of the invention provides a positioning method and an electronic device. The method includes the steps: performing first distortion correction for a first image according to a first distortion correction rule to obtain a second image; performing second distortion correction for a second image according to a second distortion correction rule to obtain a third image; acquiring M image calibration coordinates in the third image and N projection calibration coordinates in a projection area; determining the mapping relation between an image coordinate and a projection coordinate according to the M image calibration coordinates and the N projection calibration coordinates; analyzing the third image to obtain a first light spot image coordinate of a first light spot in the third image; and acquiring a first light spot projection coordinate of the first light spot in the third image according to the mapping relation between the image coordinate and the projection coordinate. Therefore, the method solves the technical problems that images are unstably calibrated, and the mapping relation between an image coordinate and a mapping coordinate is inaccurate in the prior art.

The invention discloses an image stitching tampering detection method, relates to the field of image analysis, and is an image stitching tampering detection method based on a mixed domain attention mechanism and a cavity space pyramid pooling module, and the method comprises the steps of extracting a depth feature map F of an input image; obtaining a feature map Ffinal of the tampered area by adopting a mixed domain attention mechanism; adopting a cavity space pyramid pooling module to obtain a final positioning mask M; training a stitching tampering detection method based on the mixed domainattention mechanism and the cavity space pyramid pooling module. According to the measurement of image stitching tampering detection based on the mixed domain attention mechanism and the cavity spacepyramid pooling module, the defects that in the prior art, the tampering area of a stitched image cannot be accurately positioned based on a certain specific hypothesis, and a tampering target with asmall area is easily ignored in detection are overcome.

The invention belongs to the technical field of computer network security, and relates to an anti-attack reliable wireless sensor network node positioning method. In ordinary positioning methods, nodes to be positioned are easy to attack by malicious nodes, so the positioning effects of the nodes are influenced. By the reliable positioning method provided by the invention, Sybil attacks and wormhole attacks can be resisted at the same time. The method comprises that: a beacon node estimates the smallest hop number between the beacon node and other beacon nodes; the beacon node estimates an average hop distance among the nodes; the nodes cooperate with one another to detect attack nodes in a network; and unknown nodes estimate own coordinates. Compared with the prior art, the invention improves the attack resistance of the wireless sensor nodes in a positioning process, reduces positioning errors in an environment with the malicious nodes, and reduces the communication cost and calculation cost of the nodes as much as possible in the positioning method by fully combining the characteristic that the energy of the wireless sensor nodes is finite.

The invention discloses a method for detecting spissatus and spissatus shadow Landsat thematic mapper (TM) images and Landsat enhanced thematic mapper (ETM) images. The method comprises steps of dividing an input image into 16 sub-atlases and performing wiener filtering denoising and normalization for the 16 sub-atlases; performing rough detection for the spissatus and the shadow in the 16 sub-atlases and selecting reference pairs from a rough detection result; solving a mass center connection inclined angle and a space of a final reference pair in accordance with all reference pairs; matching the spissatus and the shadow in the 16 sub-atlases in accordance with the mass center connection inclined angle and the space of the final reference pair and performing supplementary detection for unmatched spissatus and unmatched shadow; adding a matching result of the spissatus and the shadow and a supplementary detection result and obtaining final detection result sub-images of all sub-atlases; and sequentially splicing final detection result sub-images of all sub-atlases and obtaining a final detection result image. Auxiliary information and manual intervention are not required, the detection precision is high and the method can be used in detection and classification of remote sensing image variation and pre-processing of image segmentation.

A method for eliminating errors in foreground object detection in digitized images comprises providing a reference camera and a secondary camera, vertically aligning each said camera with a baseline that is approximately perpendicular to a ground plane, wherein said reference camera is placed lower than said secondary camera, selecting a foreground pixel in a reference view of a first point in a foreground object, finding a conjugate pixel of the foreground pixel in a secondary view, using the foreground and conjugate pixels to determine an image base pixel of a base point in the reference view, wherein said base point is a point on the ground plane below the first point, and using the foreground and image base pixels to find a location where the ground plane is first visible.

The invention discloses a multi-scale license plate precise locating method based on affine correction. Firstly, candidate license plate plaques are detected by using texture information and color features of license plates, then affine correction is carried out on each candidate license plate, an energy function is defined to accurately locate the positions of the license plates, noise is removed, and the positions of license plates in original images are obtained through back calculation. According to the multi-scale license plate precise locating method based on the affine correction, the license plates can be accurately located from multi-scale images in a complex environment, high robustness, accuracy and high-speed performance are achieved, and the omission rate of the license plates is close to zero. The affine correction is uniquely introduced, the license plate locating problem brought by view angles of the images is well solved, and a basis is established for the accurate locating of the license plates. Meanwhile, the energy function is originally defined to evaluate and accurately locate the license plates, more deep information beneficial to the locating of the license plates is used in the method, and the locating of the license plates is made to be more accurate, more stable and more reliable.

The invention belongs to the technical field of power transmission and transformation equipment routing inspection, and particularly relates to a power transmission and transformation equipment fault routing inspection core system based on ultrasonic waves and infrared thermal images. The power transmission and transformation equipment fault routing inspection core system based on the ultrasonic waves and the infrared thermal images comprises a data acquisition module, a computer, a wireless communication module and a battery power supply system, wherein the battery power supply system is connected with the data acquisition module, the computer and the wireless communication module, the data acquisition module comprises an infrared thermal image sensor, an ultrasonic wave sensor, a protective cover, a holder and a network switch, the wireless communication module comprises a wireless broadband terminal and antennas, the infrared thermal image sensor and the ultrasonic wave sensor are installed in the protective cover arranged on the holder, and the network switch is connected with the ultrasonic wave sensor, the infrared thermal image sensor and the computer. The power transmission and transformation equipment fault routing inspection core system based on the ultrasonic waves and the infrared thermal images has the advantages that the ultrasonic wave technology and the infrared thermal image technology are comprehensively used for detecting faults of electrical devices, accuracy of fault diagnosis is improved, the frequency of missing inspection and the frequency of incorrect inspection are reduced, and the power transmission and transformation equipment fault routing inspection core system based on the ultrasonic waves and the infrared thermal images is high in efficiency, convenient to use, flexible and the like.

The invention relates to a rubber sealing ring surface defect detection method based on machine vision, and belongs to the field of detection. The invention designs a novel sealing ring surface defectmethod aiming at the defects of poor universality, easiness in missing detection and false detection and the like of a defect detection mode in an automatic detection link based on machine vision onan O-shaped rubber sealing ring production line. According to the method, a data driving strategy is adopted, image texture features extracted by Gabor filters of different scales and in different directions are integrated and combined with a support vector machine (SVM), an image preprocessing technology and feature dimension reduction are assisted, and a classifier capable of accurately identifying the surface defects of the O-shaped rubber sealing ring is trained. The classifier can effectively reduce the missing detection rate and the false detection rate of a traditional Blob detection method (image preprocessing, image segmentation, region of interest separation and defect recognition), and is higher in universality.

The invention discloses a method and a device for monitoring energy consumption abnormity. The method comprises the following steps: monitoring an energy consumption parameter of a current period of a vulcanization process in a tyre manufacturing process; according to the value of the energy consumption parameter of the current period obtained through monitoring, obtaining an energy efficiency monitoring value and an energy efficiency predicting value of the current period of the vulcanization process in the tyre manufacturing process; according to the obtained energy efficiency predicting value and a preset confidence coefficient, obtaining the energy efficiency prediction interval of the current period; when the energy efficiency monitoring value is beyond the energy efficiency prediction interval, determining that abnormity occurs in the energy consumption of the current period of the vulcanization process in the tyre manufacturing process. The method and the device can provide reasonable monitoring for complex and variable energy consumption situations in the industrial production process, missing detection and false detection are reduced, and the precision of energy consumption abnormity monitoring is improved.