52 results about "Automatic target detection" patented technology

The invention discloses a method for extracting target closed contour based on shape prior, belonging to the field of computer application technology. From the point of view of simitating the information of human vision, the extraction method inhibiting salient edges of noise and texture is adopted, the shape prior is blended on the basis, and a new target closed contour extraction algorithm can be provided by utilizing curve evolution technology based on differential geometry, so that the important technology is provided for automatic target detection. The experiments on a plurality of imagelibraries prove that when the image background is very disordered and has partial shading, and part of the target boundary is indistinct caused by weaker light, the method can obtain the whole targetclosed contour. Simultaneously, when only a few of templates of a certain category of objects are provided, the method can extract the contour of the category of objects with different postures, namely, the method has invariance property for elastic deformation to a certain extent, so as to be directly applied to intelligent image processing. The method can accurately extract the target closed contour and has wide applicability.

The invention relates to a remote sensing image target detection method and device. The method comprises the following steps: firstly, establishing a depth full-convolution neural network; the depth full-convolution neural network comprises a main network used for feature extraction and a sub-network used for target detection; training the main network through an image classification database; after the main network is trained, adding the sub-network; fine-adjusting the network parameters by means of the remote sensing target detection database to obtain a trained depth full-convolution neuralnetwork; and finally inputting the remote sensing image to be detected into the depth full-convolution neural network to obtain a final target detection result. Compared with a conventional way of directly using a remote sensing target detection database to train the established depth all-convolution neural network, the generalization ability and accuracy can be improved, and the rapid, high-precision and high-robustness automatic target detection can be realized in a complex background.

Methods and apparatus are provided for recognizing particular objects of interest in a captured image. One or more salient features that are correlative to an object of interest are detected within a captured image. The captured image is segmented into one or more regions of interest that include a detected salient feature. A covariance appearance model is generated for each of the one or more regions of interest, and first and second comparisons are conducted. The first comparisons comprise comparing each of the generated covariance appearance models to a plurality of stored covariance appearance models, and the second comparisons comprise comparing each of the generated covariance appearance models to each of the other generated covariance appearance model. Based on the first and second comparisons, a determination is made as to whether each of the one or more detected salient features is a particular object of interest.

The invention discloses an ultrasonic targeted electric flower and fruit thinning machine, which comprises a working frame, a rotating shaft, a flower and fruit thinning adhesive tape set, an ultrasonic sensor set, a flower and fruit thinning motor, an angle adjusting mechanism, a telescopic rod and a control mechanism, wherein the working frame is in a frame structure with one open side; the open side of the working frame is provided with the rotating shaft; the flower and fruit thinning adhesive tape set is arranged on the rotating shaft; one end of the rotating shaft is provided with the flower and fruit thinning motor; the working frame is provided with the ultrasonic sensor set; the angle adjusting mechanism and the telescopic rod are arranged on the end face below the open side of the working frame respectively; and one end of the telescopic rod is connected with the working frame, the other end of the telescopic rod is a handheld end, and the handheld end is provided with the control mechanism. The flower and fruit thinning machine can realize automatic target detection, and has wide operation range, humanized design, high operation efficiency and strong fruit tree applicability.

The invention discloses a mining area automatic semantic segmentation method in remote sensing image in the technical field of automatic target detection and depth learning, characterized in that: thespecific steps are as follows: 1, establishing a training sample set: acquisition of Remote Sensing Images of Mining Areas, and artificial delineating the boundaries of the mining area, forming a Boundary Grid File, 448*448 being generated by ArcGIS, 512*512 fishing nets of two scales, the remote sensing image of the mining area being cut in batches by using the two generated fishing nets to generate image blocks of different sizes as input data of the depth learning network, and the boundary files of the mining area grid in the image being cut through the fishing nets to generate boundary files corresponding to each mining area image block as label data of the network. The invention adopts a hybrid network Den-Res Net can abstract the extracted features while preserving the integrity offeatures, which can be used to solve the redundancy problem of Dense Net network, and the method has high efficiency, automatic semantic segregation and high accuracy.

The invention belongs to the field of radar automatic target detection, and discloses a polarization SAR ship detection method based on low-rank dictionary learning and sparse representation. The method is applied to ship target detection in a polarization SAR image and specially comprises the following steps of firstly extracting a sea clutter sample as a training clutter sample, constructing a training data matrix Z through pixels of the clutter sample, secondly, utilizing the training data matrix Z for performing training, obtaining a low-rank dictionary D through learning, thirdly, performing sparse representation on feature vectors of all the pixels of a test sample on the basis of the low-rank dictionary D obtained by learning, defining the detection statistic relying on a scattering mechanism according to a solved sparse representation coefficient, setting a threshold of the detection statistic, performing threshold detection on the detection statistic of all the pixels of the test sample, and obtaining a final binary image for displaying the detection result.

The invention discloses an automatic video-target detecting and tracking method based on motion information and sparse projection. The automatic video-target detecting and tracking method is used for solving the technical problem that an existing automatic target detecting and positioning tracking method based on forward-backward motion historical images is poor in tracking accuracy. According to the technical scheme, firstly, by means of forward images and the motion information of the forward images, detection of a moving object on the images is finished automatically; secondly, the detected moving object on the images is tracked in real time in a sparse projection mode; finally, by means of statistics of motion trail information, a tracing result of a real moving target is analyzed, and influences from noise targets are removed. Tests show that the detecting and tracing result accuracy reaches more than 85%.

The invention discloses an oil and gas pipeline inspection method based on an unmanned aerial vehicle, and the method comprises the steps: carrying a high-resolution imaging system and a high-precision positioning device by the unmanned aerial vehicle to carry out low-altitude aerial photography, and obtaining continuous oil and gas pipeline images and the position information of the unmanned aerial vehicle; Performing abnormal target detection by adopting a target detection algorithm based on a neural network to obtain an abnormal target; And carrying out abnormal target positioning by adopting an algorithm based on a motion recovery structure. The oil and gas pipeline inspection method provided by the invention is relatively high in intelligent degree, an automatic target detection algorithm is adopted, and target positioning can be realized without adding additional ground control points; The oil and gas pipeline inspection method provided by the invention is relatively high in efficiency; In the abnormal target detection stage, the possible position of an abnormal target is effectively determined through an image segmentation algorithm based on SIFT and Graph Cut, and in the abnormal target positioning stage, under the condition that dense reconstruction, DEM generation and other steps in a traditional algorithm are omitted, high positioning precision can still be obtained.

The invention discloses a superpixel local information measurement-based polarized SAR ship target detection method, and mainly aims at solving the problem that the target detection rate lower complicated scenes is low. The method comprises the following steps of: 1, carrying out superpixel segmentation on an original image so as to obtain superpixel segmentation results under different scales; 2,for the segmented results, calculating three superpixel level-based difference measurements by utilizing a sliding window model; 3, converting the superpixel level-based difference measurements intopixel level-based difference measurements; 4, mapping pixel-level difference measurement vectors into different measurement values by utilizing kernel fisher discrimination, so as to a difference measurement value of each pixel point; and 5, classifying the difference measurement values of the pixel points by utilizing a linear SVM classifier, determining a category of each pixel and carrying outautomatic target detection. The method is capable of enhancing the target detection performance under complicated scenes an realizing the automatic detection process, and can be used for subsequent ship target discrimination, recognition and classification.

The invention mainly belongs to the technical field of target invasion detection and particularly relates to an unmanned aerial vehicle image-based region invasion target detection method. The method is used for target detection of an unmanned aerial vehicle. The method comprises the steps of performing Gaussian pyramid layering on an original image in an original video obtained by a vehicle-mounted camera of the unmanned aerial vehicle to lower calculation complexity of feature point extraction; and then extracting SIFT feature points of the image to perform image registration, capturing motion information in the image by adopting LK sparse optical flow of a pyramid to realize target point motion calculation, performing motion point clustering and false target removal, and finally performing target judgment to realize target detection. According to the method, the search range of inter-frame feature points of the unmanned aerial vehicle video can be reduced, the motion large-displacement problem in the unmanned aerial vehicle image is solved, and the detection capability is improved; and therefore, the manual detection intensity of region invasion is reduced and the automatic perception capability of the unmanned aerial vehicle is improved.

It is morphology filter automatic object detection method, which comprises the following steps: first to collect training samples to form heredity arithmetic for optimization train, wherein, the arithmetic adopts new range discretization code and self-adapting major / minor cross and dissociation operator; to use collected sample to train the structure element value by heredity arithmetic to form morphology filter based on Top-Hat operator; to filter the infrared object image; finally to do the division based on self=adapting threshold to detected multiple weak spots; to detect the object spots with high signal to noise rate by use of fixed threshold division.

The invention discloses a motorized station automatic target detection and spatial positioning method based on YOLO-SIOCTL. The method comprises the following steps that: a mobile station acquires hardware configuration; The carrier platform of the vehicle-mounted mobile measurement system carries out image acquisition in the process of driving at a normal speed; Realizing geographic object automatic target detection based on a YOLO algorithm; back calculation of a geographic pixel frame of the image pixel frame is cahieved based on an SIOCTL algorithm; and a single-object multi-space positionpoint is fitted with an optimal position point. The method can be widely applied to the field of patrol supervision, the workload of manual target checking is effectively reduced, the patrol efficiency is improved, the consumption of manpower and financial resources is reduced, and the method has a good application value in practical application.

The invention discloses an attention mechanism-based SAR image target detection method and a test platform, and the detection method comprises the steps: extracting advanced features, except color andshape, of a synthetic aperture radar image at a feature extraction stage by adopting a global attention module, and at a candidate frame extraction stage, using a local attention module to obtain a more accurate and effective candidate region, and performing target prediction on the feature pyramid with the advanced features by a prediction frame based on a classification regression network to obtain a detection result; and enabling the simulation test platform to achieve the functions of image preprocessing, target detection and recognition and the like, and forming a set of complete automatic target detection and recognition algorithm ground simulation test platform based on big data analysis. According to the detection method, through verification of the simulation test platform, goodrobustness and high detection accuracy are further verified.

The invention provides a line-of-sight artillery target-scoring system and method. The system comprises a laser detection automatic target detection module, a control module, a target-scoring module and a wireless communication module. The laser detection automatic target detection module is used for forming a laser detection fan. According to the line-of-sight artillery target-scoring system andmethod, a fan light curtain is formed through two laser beams swinging at a high speed, and when pellets penetrate through the fan light curtain, the pellets can be irradiated by the laser beams and reflection waves can be formed. A laser receiving probe set is arranged below a target surface; and when the laser receiving probe set catches the pellet reflection waves, the system can calculate theair positions of the pellets according to rotation angles, detected by angle sensors connected with two laser emission devices, of the laser beams and the relative distance between two laser devices,and the aim of line-of-sight artillery live-ammunition firing precise target scoring is achieved.

The invention provides a commodity target detection method and device based on eyeball tracking, and the method comprises the steps of collecting a commodity image in a visual field range of a user; determining attention point position information of the user on the commodity image through an eyeball tracking platform; inputting the commodity image and the attention point position information intoa target learning model, wherein the target learning model is used for determining a target commodity area in the commodity image according to the attention point position information; and obtainingthe commodity information in the target commodity area, wherein the commodity information comprises the category of the commodity, so that the information is tracked according to eyeballs; the attention point position data is acquired, the target learning model is employed to carry out commodity target detection, the problems of slow detection speed and low positioning accuracy in the existing commodity image target detection method are solved, the method has the advantages of fast detection speed and high detection accuracy, and the method can be used for carrying out the automatic target detection on the commodity concerned by the attention point of the user in real time.

The invention discloses an SAR image-oriented automatic target detection method and system, and the method comprises the steps: inputting an input image into a pre-trained feature fusion and anti-interference network FFAN to obtain a target frame for target detection, wherein the feature fusion and anti-interference network FFAN comprises a backbone network, a pooling and channel shuffling module, a deep fusion module, a space attention mechanism RSAM, a space feature fusion module ASFF and a classification regression network. According to the method, the features can be effectively extracted and fused, the regression precision is improved, and the detection effect is remarkably improved on the premise that the test speed is hardly influenced by the feature fusion and anti-interference network FFAN. The method has the advantage of good universality, can be used for realizing high-precision rapid detection of an airplane, and can also be used for detecting and identifying other dense targets in an SAR image.

The invention discloses a THz-QoMIMO framework suitable for a Terahertz security check instrument, comprising signal processing and target recognition digital hardware, Terahertz MIMO array front end high frequency hardware, and one-dimensional quasi-optics focusing and scanning, and employing an MIMO array layout. By utilizing MIMO synthetic focusing, a system frame rate is increased, and sending and receiving channels are reduced so as to reduce system costs; in addition, the THz-QoMIMO framework can simplify a system whole machine structure, improve system work reliability, perform repeated multi-angle measurement, reduce coherent speckle influence, and facilitate automatic target detection and recognition; besides, the THz-QoMIMO framework employs an image classification recognition method with distance information z as the primary and intensity information I as the complement, and is especially suitable for an active Terahertz rapid security check instrument.

In some embodiments, a device may include a laser rangefinder circuit including an emitter configured to direct a focused beam toward a view area and including one or more optical sensors configured to receive light including ambient light and reflections associated with the view area. The device may further include a processor coupled to the laser rangefinder and configured to determine an edge of an object within the view area based on measurements of the reflections.

The invention relates to an undercarriage safety monitoring method based on course line and visible data fusion. The method comprises the steps of providing an image monitoring module arranged at an airfield runway and a main control system arranged in a main control room and matched with the image monitoring module; searching, by the image monitoring module, a target aircraft on four sides of a rectangular traffic pattern, and when the target aircraft appears on four sides to five sides of the rectangular traffic pattern, i.e., appears a target aircraft undercarriage place, performing, by the image monitoring module, automatic target detection on the target aircraft, tracking and analyzing the target aircraft via the main control system, judging whether the target aircraft undercarriage is laid down, and then providing an early warning signal. The method greatly facilitates acquisition and analysis on the state of the aircraft undercarriage in the flight process, can judge the flight state of the aircraft, and provides technical and data support for planning of the course line and the laying-down strategy of the aircraft undercarriage.

The invention discloses an intelligent image processing device, which comprises an image sensor, a control circuit, a processor, a power supply and an interface circuit. Image signal transmission between the image sensor, the control circuit, the processor, the power supply and the interface circuit is bidirectional. The processor is used for performing calculation on received image data so as to achieve automatic exposure, automatic focusing, automatic white balance, automatic tracking, automatic target detection and automatic image intensification. Namely, the multiple functional modules of the intelligent image processing device are integrated and the signal transmission relation between the units is fully utilized so as to achieve advanced intelligent judgement about scenes or statuses, such as the functions of target detection, recognition, tracking and the like, accordingly the image processing device is more intelligent, and the intelligent level of the image processing device is raised.

A coordinate measuring device includes a carrier that can be rotated automatically about two axes and that can be directed toward a measuring aid. The following are arranged on the carrier so as to be able to move together: an optical distance measuring device for measuring the distance to the measuring aid; a light source for emitting light, directly or by means of optical elements, wherein said light is visible as a target point when reflected on the measuring aid; a target detecting unit for determining a position as the position of the imaging of the target point on a position detection sensor. The control apparatus is designed to direct the carrier at the measuring aid by rotating the carrier about the at least two axes of the carrier according to the fine position and the rough position; and the light source is a superluminescent diode (SLED).

The invention discloses a YOLO-SITCOL-based automatic target detection and spatial location method of a fixed station. The method comprises the following steps: configuring acquisition hardware of thefixed station; acquiring an image; based on a YOLO algorithm, realizing automatic target detection on a geographic object; based on an SITCOL algorithm, realizing reverse calculation on a geographicpixel frame by virtue of an image pixel frame; and fitting the best position point by virtue of multiple spatial positions of a single object. The method disclosed by the invention effectively reducesworkload of manually viewing a target, improves inspection efficiency, reduces consumption of manpower and financial resources and has a better application value in practical application.

The invention discloses an automatic detection and tracking method based on target adaptive projection. The method comprises the following steps: reading to-be-tracked video sequence information, andperforming RGB-to-Gray color space conversion on each frame of image; solving a foreground target mask in the first M frames by utilizing a weighted sliding variance method; adaptively selecting the maximum mask of the foreground target area; carrying out horizontal and vertical projection on the mask; searching the projection result in the vertical and horizontal directions, and determining the center of the optimal target through accumulated pixels and the proportion; performing round-trip detection in the vertical and horizontal directions from the center position, determining the optimal target scale through accumulated pixels and the proportion, and thus determining an ROI (target area); and finally, tracking the target by using a centroid method to finish an automatic target detection and tracking process. The method is easy to implement and clear in process, and the influence of background noise can be effectively avoided in the ROI extraction process through the self-adaptive projection method.

The invention discloses an automatic targeting detection device for agriculture. The device comprises an unmanned aerial vehicle assembly, a camera assembly, a light source assembly and a control unit; the unmanned aerial vehicle assembly comprises a rack, a connecting rod is arranged at one end of the top of the rack, a supporting rod is connected to one end of the bottom of the rack, and the supporting rod is connected with a protective rod; and the light source assembly comprises a light source generator, one end of the light source generator is connected with a storage battery, the other end of the light source generator is connected with a collecting lens, and the light source assembly is arranged on the inner side of the supporting rod and connected with the rack. The unmanned aerial vehicle is used for carrying out camera shooting detection, higher flexibility and convenience are realized. Compared with the cost other technologies, the cost of the device is lower; on one hand, a view blind area which is difficult to detect by other technologies (such as a remote sensing technology) can be detected, on the other hand, the illumination degree is freely adjusted according to a previous light condition, and the reliability during camera shooting is improved, and the accuracy of an analysis result is improved.

The invention belongs to the technical field of automatic target detection, and discloses an unmanned aerial vehicle target detection system, a target detection method and a computer readable storage medium, and the unmanned aerial vehicle target detection system comprises an unmanned aerial vehicle system which is used for carrying a photoelectric load system to an observation position and hovering at a fixed point; the photoelectric load system is used for executing a fixed-point observation task and performing fixed observation of a target range area; the wireless image transmission system is used for transmitting observation video images by adopting an HHLM type transmission module; the wireless remote control system is used for controlling the unmanned aerial vehicle system and the photoelectric load system; and the target detection comprehensive task station is used for receiving, storing, forwarding and processing video images. The marine target detection system provided by the invention is of great significance in improving the testing technology and testing equipment level and improving the actual combat ability and training level of ship gun shooting of a driving and protecting ship, and meanwhile, has very strong practical significance in guaranteeing the live shell shooting quality of the ship gun and adapting to battlefield requirements.

The invention discloses a THz-QoMIMO framework suitable for a Terahertz security check instrument, comprising signal processing and target recognition digital hardware, Terahertz MIMO array front end high frequency hardware, and one-dimensional quasi-optics focusing and scanning, and employing an MIMO array layout. By utilizing MIMO synthetic focusing, a system frame rate is increased, and sending and receiving channels are reduced so as to reduce system costs; in addition, the THz-QoMIMO framework can simplify a system whole machine structure, improve system work reliability, perform repeated multi-angle measurement, reduce coherent speckle influence, and facilitate automatic target detection and recognition; besides, the THz-QoMIMO framework employs an image classification recognition method with distance information z as the primary and intensity information I as the complement, and is especially suitable for an active Terahertz rapid security check instrument.

The present invention designs a naval gun system accuracy detection device and detection method based on image processing technology. The image sensor is arranged on the zero-flying instrument, and the image sensor is used to collect the image in front of the artillery fire axis. The front image and its data information and the target tracking image and its data information of the tracker are all transmitted to the computer, and the computer extracts the tracking target from the target tracking image of the tracker according to the range selected by the tracker wave gate, and the tracking target and Template matching is performed on the image in front of the artillery axis, the similarity is calculated according to the matching algorithm and the matching result is output, the position of the target tracked by the tracker is located in the image in front of the artillery axis, and the average zero-fly error is calculated. It has integrated optimization features such as automatic target detection and recognition, image processing analysis, and zero-fly error self-correction. It can use close-range corner targets to detect comprehensive errors of naval gun systems.

The invention discloses an automatic aiming laser based on spatial spectral coding. The laser comprises: a gain medium (1), a dispersion element (2), a laser transmitter (3), an attack target (4), and a scattered light receiver (5) ), pump (6), the output end of the gain medium (1) is connected to the input port of the dispersion element (2), and the output end of the dispersion element (2) is connected to the laser transmitter (3), when the attack target (4) Scattered light is emitted when entering the sensitive area, the scattered light receiver (5) is connected to the first input end of the gain medium (1), and the pump (6) is connected to the second input end of the gain medium (1). The laser provided by the invention is a brand-new laser, which has the beneficial effects of simple structure, no mechanically adjusted parts, target detection and automatic aiming.