931 results about "Track algorithm" patented technology

The present invention relates to an adaptive sensing-based lightweight monitoring method for a fine crack in a complex background region. The method comprises the following steps: step S1, on the basis of region division, performing automatic acquisition of crack information, wherein PTZ camera sensors are used to automatically perform block-wise acquisition on crack regions; step S2, performing an adaptive complex scale calibration process, using a multi-scale template matching algorithm to adaptively correct distortion information of all regions, and performing real-scale conversion from pixel precision; step S3, constructing a lightweight crack segmentation network to process data processed in step S2; and step S4, by means of a quantitative crack-tracking algorithm based on Euclidean distance similarity classification, performing real-time monitoring on each piece of crack dynamic information. Compared with the prior art, the present invention has advantages such as achieving efficient, accurate, and online monitoring and analysis of cracks.

The invention relates to the technical field of marine monitoring, in particular to a marine target visual servo tracking method, which comprises the steps of multi-modal sensor fusion, a self-adaptive visual tracking algorithm, a servo control and visual collaboration mechanism and a shielding processing and target re-identification strategy. The problem that tracking is unstable under the conditions of illumination change, ship body shaking, target shielding and the like in a traditional method is solved. The IMU, the GNSS and the visual data are fused through extended Kalman filtering, ship body shaking is compensated, and the target state estimation precision is improved; the improved D-Fi ne target detection model is combined with an online feature updating mechanism to dynamically adapt to the appearance change of the target; the prediction and correction control strategy and the double-closed-loop PI D controller cooperate to adjust the camera holder, and the tracking delay is reduced; the multi-clue shielding detection and space-time joint feature matching technology ensures accurate re-identification of the target after shielding is removed. The real-time performance and robustness of the tracking system on an embedded platform are improved, and an efficient and stable target tracking solution is provided.

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention discloses a millimeter wave radar human body tumble detection method based on multi-feature fusion, and the method comprises the steps: obtaining an original point cloud frame from each radar, and carrying out the timestamp calibration and space coordinate system conversion; performing noise filtering, ground segmentation and human body point cloud extraction on each frame of point cloud; dividing the preprocessed point cloud into a static cluster and a dynamic cluster; filtering false dynamic clusters; extracting a residual dynamic cluster set, and identifying and tracking the human body dynamic clusters in continuous frames by adopting a tracking algorithm; extracting a corrected time sequence feature from the tracked human body cluster; inputting the time sequence characteristics into a pre-trained deep time sequence network, learning a falling time sequence dependency relationship, and outputting an abnormal index; and performing multi-source fusion with the abnormal index to obtain a comprehensive index to judge whether to trigger an alarm. The method can adapt to a complex home environment, improves the detection accuracy and real-time performance, and reduces the false alarm and missing alarm.

The invention relates to the technical field of artificial intelligence image processing, and particularly discloses a camera-crossing vehicle target tracking method, system and device in an expressway tunnel based on deep learning and a medium, and the method comprises the steps: image collection, image preprocessing, detection and recognition of the preprocessed image through a trained YOLOv5 model, and detection and recognition of the camera-crossing vehicle target in the expressway tunnel through the trained YOLOv5 model. A vehicle in an image is identified, a detection frame is generated for the vehicle, and then the type and color of the vehicle, vehicle re-identification Re-id, single-camera vehicle target tracking and cross-camera vehicle target tracking are marked. According to the method, image preprocessing model adaptive image enhancement is combined with YOLOv5, so that the type and color of the vehicle can be accurately detected under foggy days and low-illumination conditions, the feature vector after Re-id is added on the basis of the ByteTrack tracking algorithm to serve as track matching of a low-confidence detection frame, and compared with a traditional ByteTrack tracking algorithm, the method has the advantages that the method is simple and convenient to operate, and the detection efficiency is improved. Through the feature vector output by the ResNet101-IBN model, the static feature of the target can be obtained in a more detailed manner, and the accuracy is high.

The invention discloses a photovoltaic tracking method and system based on deep learning optimization scheduling. The method comprises the following steps: a photovoltaic tracking system and an environment data acquisition device acquire initial information; after the initial information is obtained, a tracking camera of the environmental data acquisition device obtains the relative position of the sun by using a sun-oriented movement trajectory tracking algorithm model, then calculates a tracking angle according to the position information of the sun and drives the camera to rotate to a theoretical angle so as to realize tracking of the sun; while tracking the sun, the tracking camera captures and captures an image of weather near the sun rich in weather characteristic information, and the captured image information, data acquired by the environmental data acquisition device and historical photovoltaic power generation power data recorded by the MPPT solar controller are sent to the photovoltaic tracking controller; and the photovoltaic tracking controller makes different control strategies according to the contents. The probability of invalid tracking of the system in a complex environment is reduced, so that the photovoltaic tracking system is based on the optimal state, and the power generation efficiency is improved.

The invention provides a multi-target tracking method based on a YOLOv8 model and a Byte Track algorithm, and relates to the technical field of computer vision and edge equipment. The method specifically comprises the following steps: acquiring data of a plurality of images, performing format conversion, and constructing an image data set; constructing a DC-YOLOv8 network structure, and performing training by using the image data set to obtain a target detection model based on the DC-YOLOv8 network structure; obtaining a to-be-detected video stream, extracting continuous image frames from the to-be-detected video stream, and preprocessing the extracted image frames; and inputting the preprocessed image frames into the target detection model for target detection, and performing target tracking on all detected targets by adopting a Byte Track algorithm to obtain tracking trajectories of the tracked targets in all the image frames and generate a video stream. According to the invention, target tracking can be carried out on a video with many targets more smoothly.

The invention relates to the technical field of intelligent unmanned sweeping, in particular to an unmanned sweeping vehicle edge path tracking control method and system. Through cooperative work of a path planning and edge feature detection module, a real-time positioning and attitude estimation module, an edge tracking control module, an obstacle detection and avoidance module, a path correction and feedback module and an energy consumption optimization and endurance control module, precise tracking of the sweeper on an edge path in a complex environment is realized. Through multi-sensor data fusion, extended Kalman filtering and a self-adaptive path tracking algorithm, the system can adjust the driving state of the motor sweeper in real time, it is ensured that the motor sweeper is driven accurately along the edge, through a dynamic path planning algorithm and an energy consumption optimization model, the ability of the motor sweeper in the aspects of obstacle avoidance, path correction and endurance control is effectively improved, and the motor sweeper driving stability is improved. Therefore, the working efficiency and the endurance time are improved on the premise that the cleaning task requirement is met. The device is suitable for various complex sweeping environments, and efficient edge sweeping operation is achieved.

The invention provides a multi-target tracking algorithm of a wide-view-angle feature fusion memory network, and belongs to the technical field of computer vision and artificial intelligence. The technical problem that the tracking performance is insufficient when a long-distance small-size target and a fast moving target are processed under the view angle of the unmanned aerial vehicle is solved. According to the technical scheme, the method comprises the following steps: S1, video frame preprocessing and target detection; s2, carrying out deep fusion on the picture feature information in a wide-view fusion memory network; s3, track confidence modeling is introduced in the filtering process of adaptive motion filtering AMF; and S4, in the first step of association, performing first association through a motion feature extraction model MFEM. The method has the advantages that the target position is adjusted and optimized step by step through dynamic updating of a tracking algorithm, and finally information of all tracks of the target is obtained.

The invention discloses a multi-unmanned ship cluster cooperative tracking control method based on data driving, and belongs to the field of unmanned ship cluster intelligent control. The method comprises the following steps: firstly, constructing a communication topology for cluster distributed cooperative control of multiple unmanned ships; constructing a single unmanned ship speed and course system dynamic linearization data model with interference terms based on unknown external interference and nonlinear uncertainty; constructing a smooth anti-interference trajectory tracking algorithm of a single unmanned ship tracking dynamic target point; constructing a pseudo-Jacobian matrix estimation and distributed disturbance compensation controller for distributed model-free adaptive control of the multiple unmanned ships; constructing a reset algorithm and a controller of an actual unmanned ship; and finally, multi-unmanned ship cluster cooperative tracking control is realized. The method breaks through the limitation of a traditional control method, does not need to depend on complex system model information, only utilizes input and output data of the system, and achieves a complete data-driven intelligent control strategy.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention relates to the technical field of underwater acoustic navigation, and provides an underwater acoustic navigation method and system, a medium, equipment and a computer program. The underwater acoustic navigation method comprises the following steps: calculating a sound ray total displacement value by adopting a sound ray tracking algorithm based on underwater carrier position information, transponder position information, and one-way propagation time and a sound velocity profile of a signal between an underwater carrier and a transponder; constructing an acoustic navigation observation model based on the underwater carrier position information, the transponder position information, the total displacement value of the sound rays, and depth and elevation anomalies at the underwater carrier; defining a weighting function according to the one-way propagation time and the dynamic value of the signal between the underwater carrier and the transponder; and solving the acoustic navigation observation model and the weighting function to obtain position information and speed information of the underwater carrier for acoustic navigation. According to the method, the navigation algorithm can be optimized from the two aspects of the acoustic navigation observation model and the weighting function, and the accuracy of acoustic navigation is greatly improved.

The invention discloses a structural deformation measurement method based on geometric correction of a multi-view panoramic camera. A method for measuring bridge displacement by using a single multi-view panorama camera is provided, multi-view segmentation is performed on a panorama image according to a panorama camera equivalent model and a cube projection method, and independent calibration and correction are performed to obtain a distorted image capable of covering a 360-degree full-field range; therefore, a single panoramic camera is equivalent to a vertical four-camera system without overlapped view fields, so that the reference target in any direction can be used for displacement correction. The invention further provides a camera disturbance elimination method based on a multi-view circle center array target and pose transformation matrix purification optimization, and a coordinate system transformation matrix after purification optimization is obtained through a high-precision multi-feature point tracking algorithm from camera single-view moving target pose measurement. And obtaining a multi-view geometric correction theoretical model of any view of the measurement target and the reference target in a 360-degree range by utilizing a multi-view equivalent camera fixed connection relationship.

The invention discloses a high-precision continuous tracking method for a strong-maneuvering infrared weak and small target under a space-based detection visual angle, and the method comprises the steps: obtaining the uncertainty measurement of a target at a non-maximum suppression stage of a detection result, and enabling the uncertainty measurement to act on a target state updating and data association stage; therefore, the experience distribution of the detector is fully transmitted to the tracking process, and the tracking accuracy is improved. In order to cope with a complex target maneuvering state, an interactive multi-model technical route is adopted to design a tracking algorithm; in order to reduce the dependence on a prior motion model, a dynamic Markov transfer matrix construction method is designed, and the model transfer probability is updated in a mode of comprehensively modulating historical dynamic information and current static information; in a data association stage, targets with different scales are associated by integrating advantages of IoU and NWD, uncertainty is transmitted to a cost calculation process, and tracks and targets which are indefinitely matched are processed based on scale invariant and energy invariant hypotheses, so that high-precision continuous tracking of the targets is realized.

The invention discloses a financial escorting process abnormal behavior identification method and system, and relates to the field of data processing. The method comprises the following steps: acquiring personnel characteristic data, and establishing a multi-modal biological characteristic database; matching features extracted from the real-time personnel feature data with features stored in a multi-modal biological feature database by using a convolutional neural network to obtain a matching result; when the matching result is that the matching is successful, continuing to obtain the video stream of the monitoring area in real time and preprocessing the video stream; performing target detection on the preprocessed video stream by using a deep learning algorithm, and identifying a tracking target; performing continuous frame tracking on the detected tracking target by using a target tracking algorithm to obtain a motion track of the tracking target; extracting attitude information of the target by using an attitude estimation algorithm, and constructing a multi-dimensional behavior feature vector in combination with a motion track and spatial-temporal features of the target; establishing a preset abnormal behavior rule base, wherein the abnormal behavior rule base comprises a known abnormal behavior set; and based on the output of the multi-dimensional behavior feature vector, in combination with a preset abnormal behavior rule base, performing real-time analysis on the identified behavior, and identifying whether the target is performing an abnormal behavior or not. The technical problems that in the prior art, risks in the escort process cannot be recognized at the same time through a single personnel recognition technology, and the requirement for timely finding out the escort risk is difficult to meet are solved, real-time comparative analysis of the movement track and the preset track is achieved through verification of personnel multi-mode biological characteristics and tracking of the movement track of the target, and the safety of the escort risk is improved. Therefore, the real-time monitoring efficiency is improved, and the technical effects of reducing the escort risk are achieved.

The invention provides a multi-head cattle identification and tracking algorithm based on improved YOLOv10s. The multi-head cattle identification and tracking algorithm comprises the steps of collecting and constructing a cattle activity video data set, constructing a multi-target cattle data set according to the cattle activity video data set, and constructing a re-identification data set according to the multi-target cattle data set; an improved YOLOv10s model is constructed, and an improved SPPF module and an improved PSA module are introduced; training the model by using the multi-target cattle data set; selecting a multi-target cattle re-identification model, and training the multi-target cattle re-identification model by using the re-identification data set; and taking the trained model as a target detector, taking the trained multi-target cattle re-identification model as a feature extraction network, and detecting a multi-head cattle to be tracked and identified based on a DeepSort algorithm to obtain a multi-head cattle tracking and identification result. By improving the network structure, the model parameter quantity is reduced, the detection and tracking process is accelerated, and performance optimization is realized.

The invention relates to the technical field of fault equipment detection, and discloses a power equipment fault detection method and system based on high-precision temperature measurement, and the method comprises the steps: scanning the surface temperature of power equipment through a high-precision temperature measurement device, obtaining a temperature distribution image, processing the temperature distribution image, and obtaining a temperature distribution matrix; carrying out gradient analysis and temperature difference identification on the temperature distribution matrix to obtain a hot spot candidate area; performing clustering analysis on the hot spot candidate region to obtain an initial hot spot region, and generating a static boundary by adopting a boundary tracking algorithm; correcting the static boundary according to the temperature change of each boundary point to obtain a hot spot area; and performing fault risk assessment according to the load data of the power equipment and the temperature data of the hot spot area. According to the invention, through temperature data analysis and region boundary correction, the hot spot region can be accurately identified, and in combination with a machine learning algorithm, the accuracy of power equipment fault risk assessment is improved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of director and picture synthesis methods, in particular to an intelligent director and picture synthesis method of a panoramic live broadcast system, which comprises the following steps: acquiring panoramic video data shot by a panoramic camera; obtaining user feedback data; based on the panoramic video data, generating a shot selection strategy by using a deep reinforcement learning algorithm; detecting a live broadcast key event in real time by using a target detection and tracking algorithm based on the panoramic video data; generating a picture shooting plan sequence according to the shot selection strategy and the live broadcast key event; performing intelligent editing and synthesis on the panoramic video data based on the picture shooting plan sequence; and outputting the synthesized live video. And updating parameters of the deep reinforcement learning algorithm based on the user feedback data, thereby realizing full automation of the program directing process, greatly improving the efficiency, and enabling the system to continuously learn from live broadcast experience through the deep reinforcement learning algorithm and gradually improve the decision-making ability of the system.

The invention discloses a pedestrian multi-target detection and tracking algorithm based on cross-layer fusion and dynamic adjustment, and aims at the problem that a deep convolutional neural network in a backbone network is frequently excessively parameterized, a composite scaling mechanism is introduced to reduce the parameter quantity and improve the feature extraction capability of the network; meanwhile, considering that targets with different scales exist in a data set, a lightweight cross-scale feature fusion module is fused at the neck of the network, so that the adaptability of the model to scale change is enhanced; besides, aiming at the problems of small targets and shielding targets, a new loss function InnerWiseWIoU is designed, and in combination with internal region optimization and context information, the target positioning precision is improved. According to the method, the new model is applied to pedestrian tracking, the shielding degree between pedestrians is calculated and the matching threshold is dynamically adjusted aiming at the condition that a fixed confidence threshold in a tracking task is not suitable for centralized and rapid change of a target, so that the tracking performance is effectively improved, and the adaptability in a complex scene is improved.

The invention relates to a tracking navigation method for positioning a crop center point based on laser radar and image fusion, and belongs to the technical field of navigation, and the method comprises the steps: carrying out the marking of a farmland image after distortion correction, generating a training sample, inputting the training sample into a U-net model, carrying out the training, obtaining a crop segmentation model, and carrying out the classification of crops and weeds; performing feature fusion on the filtered laser radar point cloud data and the farmland image after distortion correction to obtain crop point cloud with semantic information; calculating a crop center point according to the crop point cloud with the semantic information; and providing navigation for the agricultural robot by taking the crop center point as a preview point. The spatial structure information of the target crop is obtained through the laser radar and the camera, the semantic point cloud is generated through feature fusion, the crop center point is dynamically extracted from the point cloud to serve as a real-time preview target, a fixed preview distance mechanism in a tracking algorithm is replaced, centimeter-level precise navigation is achieved, and the precision of the target crop is improved. And the operation precision and the operation safety of the agricultural robot are greatly improved.

The invention discloses an unmanned aerial vehicle target tracking method based on feature fusion and sparse attention, and the method comprises the following steps: selecting improved Res2NeXt to obtain multi-scale features in order to enable a tracker to directly pay attention to an important space region and avoid the increase of the calculation amount, and carrying out the recognition of the multi-scale features through the improved Res2NeXt. Channel interdependence and space interdependence of target features are learned, self-adaption fusion is carried out, and feature representation is enhanced. A self-context enhancement module and a cross feature module are used, so that useful semantic context information can be adaptively focused to perform feature enhancement, and feature maps of each branch can be accepted to perform fusion. And introducing a regression and classification model as a prediction head to predict a target template. The method can solve the problems of target shielding and small target of the unmanned aerial vehicle, improves the local feature processing capability of the model, and effectively improves the accuracy of a tracking algorithm.

A video file may be presented via a user application that displays one or more video frames of the video file. A user request to perform an object detection for objects of a specific object type in a video frame of the video file may be received from the user application. A machine-learning model of a plurality of machine-learning models that is configured to detect objects of the specific object type may be applied to the video frame to detect an object of the specific object type in the video frame. Each of the plurality of machine-learning models may be trained to detect objects of a corresponding object type. Subsequently, an object tracking algorithm may be applied to one or more additional video frames of the video file to track the object of the specific object type across the one or more additional video frames.

The invention provides a mobile robot figure tracking comprehensive framework based on multi-target tracking. Firstly, the framework combines an improved OC-SORT tracking algorithm with a gesture detection technology, and realizes dynamic selection, accurate recognition and position estimation of a plurality of objects. Secondly, accurate path tracking of the omni-directional wheeled mobile robot can be achieved by planning a Dubins path and providing transverse and longitudinal control models based on the omni-directional wheeled mobile robot. According to the improved OC-SORT tracking algorithm, evaluation is carried out on public data sets such as DanceTrack and Person Tracking, the tracking precision and the positioning precision reach 60.5 HOTA and 98.1% respectively, and the tracking precision and the positioning precision exceed existing multi-target tracking OC-SORT and single-target tracking ODFS. The robot personnel tracking framework provided by the invention can be operated on an embedded system equipped with NVIDIA TM # imgabs0 # Jetson Xavier NX. The performance of the system on customized personnel tracking data sets and the successful application of the system in real-world scenes prove the effectiveness of the system in real-time tracking and following of individuals.

The invention provides a tunnel congestion node real-time estimation method and system based on digital twinning, and the construction of a digital twinning system physical scene of a tunnel is realized based on a Unity engine and SUMO software by determining the spatial characteristics, traffic information, camera information and the like of the tunnel. And then, dynamic and static data information of the vehicle is obtained through a CornerNet image detection network and a Deepsort tracking algorithm, the driving condition of the lost vehicle is deduced and simulated according to an acceleration distance control car-following model and a safe distance lane changing model, and dynamic target projection and full tracking of the digital twin system are realized based on an improved Hungary algorithm. And finally, node division of vehicles in the tunnel is realized according to the proposed intelligent traffic flow node model, node saturation is introduced, a node coupling congestion identification model is proposed, and tunnel congestion event sensing and congestion node positioning are realized. According to the invention, on one hand, traffic management resumption in the tunnel can be carried out by means of the digital twin system, and the congestion process and reasons are deeply analyzed; and on the other hand, the traffic flow congestion condition in the tunnel can be detected in real time, and the congestion node can be determined, so that the congestion traffic flow can be dredged in advance, and the accident occurrence probability in the tunnel can be reduced. And the method is of great significance to future digital and intelligent traffic flow research.

The present invention proposes a pulse compression radar range super-resolution method based on sparse recovery, which is mainly used to solve the problems that the existing method is not applicable to narrow-band pulse compression radar and the amount of calculation is too large. The implementation steps are: pulse compression processing without windowing radar echo data; constructing a subdivided time matrix using radar parameter information; obtaining a simulated pulse compression data matrix by subdividing the time matrix, intercepting the data on both sides of the pulse compression peak to obtain a peak data matrix, constructing a peak matching sparse dictionary matrix, using an orthogonal matching tracking algorithm to process the echo data after pulse compression to obtain a recovery matrix, extracting the target information of the recovery matrix, and obtaining a one-dimensional super-resolution range image of the radar. The present invention can improve the resolution by more than 1 times based on the theoretical resolution of traditional pulse compression processing.

The invention relates to a target tracking system based on multi-modal deep feature proxy cross attention guidance, and the method combines and uses a visible light image and a thermal infrared image, and improves the performance of a tracking algorithm under a complex illumination condition through an innovative proxy cross attention mechanism. The system comprises a multi-modal detector and a data associator, wherein the detector consists of a double-branch feature extraction network, a proxy cross attention feature enhancement module and a feature pyramid sharing convolution module; the data correlator comprises a neural Kalman filter and a low-confidence-coefficient-based detection box reuse strategy, the challenge of a complex motion scene is solved by dynamically adjusting process noise and observation noise parameters, and the long-term tracking performance is improved. According to the method, multi-modal data fusion is carried out on a deep feature level, so that target detection and tracking are optimized, and the tracking precision and stability in a complex environment are improved.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention discloses an optical imaging zoom point selection correction method and system, which can correct the deviation between a theoretical model and actual lens characteristics in real time through a peak point tracking algorithm and offset vector calculation in a second stage, effectively solve the problem of individualized errors caused by lens group assembly tolerance, material refractive index difference and the like, and improve the correction accuracy. The stratified sampling strategy of the first stage is combined with the slope interpolation of the third stage, untested points in a theoretical curve are filled, a high-precision focusing curve is generated, the full range of zooming is covered, and local focusing failure caused by traditional uniform sampling is avoided; according to the method, curve jitter caused by test noise or mechanical vibration is eliminated, a smooth zoom focusing curve is output, the anti-interference capability in actual control is improved, frequent jitter of a focusing motor is avoided, a search strategy is dynamically adjusted in combination with definition data, an actual peak point is quickly locked, and the problem of overmodulation or slow convergence caused by a traditional fixed step length is solved.

The invention discloses a cigarette hard carton damage defect detection system and method, and the system comprises an image collection unit which comprises a shooting angle adjustable camera shooting assembly and an array type LED light source assembly; the image processing unit is used for carrying out gray processing, edge detection, dynamic binarization, denoising, sharpening enhancement and the like on the acquired image, and the image detection unit is used for generating a dynamic mask area to carry out defect detection on the image based on a detected straight line and dynamic rectangular tracking algorithm. Performing spatial positioning on spots in the image based on a rectangular matching algorithm of coordinate transformation, and when detecting that a certain spot is not in any rectangular range, judging whether the spot is a defect or not by combining a grading algorithm based on a color block area threshold value; and the result execution unit is used for outputting the defect detection result to the elimination mechanism, the early warning mechanism and the display assembly. According to the invention, the embedded visual detection module is used for carrying out machine visual online detection on the damage and scratch defects of the carton, so that the sound-light alarm and elimination of the defective cigarette carton are realized, the defective cigarette carton is prevented from entering the next process, the long-time tracing caused by difficulty in finding problems is also avoided, and the production efficiency is improved. And the control capability of a workshop aiming at the carton defect quality problem is greatly improved.