423 results about "Motion blur" patented technology

The invention discloses an unmanned aerial vehicle laser and vision fusion inspection method and system for bridge bottom disease detection, and the method comprises the steps: carrying out the synchronous data collection through employing a calibrated laser radar, a camera and an IMU, and obtaining a three-dimensional laser point cloud and a two-dimensional visual image of the appearance of a bridge; sharpening the image containing the motion blur and completing brightness self-adaption of the image; stable feature points are extracted, multi-frame matching is carried out, the corresponding poses of the images are estimated, and bridge dense point cloud reconstruction is completed; performing geometric component segmentation on the point cloud to generate a geometric prior region; component segmentation is carried out on a support area in the image, and a continuous and accurate component segmentation result is obtained in combination with a geometric prior area; screening the image, calling a targeted disease detection model in a corresponding component area, and generating a segmentation mask for the disease; obtaining a real disease three-dimensional point cloud, and carrying out quantitative calculation on the physical size of the disease; and displaying the real disease three-dimensional point cloud data and the physical size of the disease. The method is high in efficiency and precision.

The invention relates to the technical field of computers, and discloses a real-time rendering and interaction method and system for an immersive virtual reality scene. The method comprises the following steps: fusing tuner inertial data and eyeball tracking data, and constructing a prospective state prediction model; generating a predictive focus field in combination with scene visual saliency; synthesizing an anisotropic temporal-spatial resolution graph according to the predicted head angular velocity; gPU variable-rate coloring is driven to realize non-uniform rendering; and re-projection or dynamic fuzzy correction is executed in a self-adaptive manner according to the attitude prediction error before display. According to the technical scheme, the perception delay and the rendering load are remarkably reduced, and the frame rate stability and the visual immersion in a high-dynamic scene are improved.

The invention provides a motion blurred bar code identification method and system based on multi-frame image fusion, and the method comprises the steps: obtaining a continuous image sequence containing a motion blurred bar code, carrying out the motion track feature extraction of the continuous image sequence, and obtaining a motion vector field and a pixel displacement track set of a bar code region in each frame image unit; performing multi-frame image fusion on the continuous image sequence based on the motion vector field and the pixel displacement track set to generate a candidate bar code image set; performing deblurring enhancement processing on the candidate bar code image set to obtain a clear bar code image unit after deblurring processing; and performing bar code area positioning and distortion correction processing on the clear bar code image unit to generate a standardized bar code image, and performing identification to obtain an identification result. According to the invention, the accuracy and reliability of bar code identification in a motion blurred scene can be obviously improved.

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention relates to the technical field of computer vision, in particular to an image target detection system and method based on deep learning, and the system comprises a dynamic feature alignment unit, a motion blur compensation unit and a feature fusion control unit. A dynamic feature alignment unit generates spatial deformation parameters through a deformable convolutional layer and an offset prediction sub-network, resamples a shallow high-resolution feature map, and realizes deep and shallow feature space alignment, and a motion blur compensation unit generates a motion vector based on brightness gradient field difference, constructs a mask and weights a suppression blur region, so as to realize deep and shallow feature space alignment. The feature fusion control unit analyzes local entropy and target size distribution, dynamically distributes feature weights and feeds back and optimizes offset parameters, a closed-loop learning loop is formed by the method, and the problems of inaccurate feature alignment, fuzzy interference and poor scene adaptation are solved.

The invention relates to the technical field of industrial automatic detection, in particular to a collaborative visual detection method and system for an SMT production line, and the method comprises the steps: obtaining a beat reference signal which is synchronous with the periodic motion beat of a mounting head in the SMT production line; determining a stable acquisition window of which the vibration intensity is lower than a preset vibration threshold value; in the stable acquisition window, sending a synchronous trigger instruction to a plurality of visual inspection cameras deployed on the SMT production line in a unified manner; performing space-time alignment processing on the multi-path image data; performing motion blur correction processing on the image data subjected to the space-time alignment processing; and carrying out validity verification on the result of the space-time alignment processing, and executing a preset exception processing strategy when the verification result indicates that the verification fails. According to the invention, the beat reference signal synchronized with the motion beat of the mounting head is directly obtained, and the opportunity decision of image acquisition and the real-time vibration monitoring result are deeply bound, so that the double strategies of beat synchronization and vibration avoidance are realized.

The invention relates to a traffic accident intelligent detection system and method based on a YOLOv12 improved architecture. The system comprises a YOLOv12 enhanced feature extraction network, a multi-scale detection head, a time sequence information fusion module, a real-time reasoning optimization engine and an intelligent decision fusion system, and realizes collaborative optimization of local feature enhancement and global context modeling by constructing six core technology modules and adopting collaborative learning of a C2f-Attention mechanism and deformable convolution. According to the method, a composite loss function special for traffic accidents is innovatively designed, and adaptive fusion of multi-scale features and difficult sample mining are realized through a multi-objective optimization mechanism of Enhanced Focus Loss, IoU-aware Loss and Severage-aware Loss. According to the method, the problems of low detection precision and false alarm and missing alarm caused by illumination variation, shielding and motion blur in a traffic monitoring scene are effectively solved, in the test of an AccidentsDesection YOLOv8 data set, the mAP at 0.5 reaches 91.27% and is improved by 8.6% compared with that of YOLOv8, the reasoning speed reaches 67 FPS, experimental results show that the system has excellent performance in the aspects of detection precision, real-time performance and model compression, and the method is suitable for popularization and application. The method achieves a remarkable effect in traffic accident intelligent identification, and has a remarkable technical effect and industrial application value.

The invention relates to the technical field of embankment inspection, and provides an embankment inspection robot environment detection method and system, which can determine a motion blur direction, extract linear features of an image and determine a linear feature direction by acquiring an embankment image and performing motion blur judgment when the image has motion blur. And on the basis, according to the geometrical relationship between the motion blur direction and the linear feature direction, whether the linear feature is the embankment crack or not is judged. By using the motion blur direction as a judgment basis, a pseudo crack and a real crack caused by motion blur can be effectively distinguished, and early-stage potential safety hazard detection omission caused by image quality reduction is avoided, so that the accuracy and the reliability of embankment crack detection are remarkably improved, and the continuity and the effectiveness of an embankment inspection task are guaranteed.

The invention relates to an automatic milling cutter setting method and system based on machine vision, and belongs to the technical field of milling cutter setting. The method comprises the following steps: firstly, positioning initial position coordinates of a milling cutter, and planning an initial tool setting path of the milling cutter by combining target tool setting position coordinates; then obtaining an image of the milling cutter in the initial cutter setting path, carrying out image denoising and image deblurring processing, carrying out edge detection after obtaining a second image, extracting an edge contour of the milling cutter, calculating sub-pixel coordinates of edge points of the contour, and carrying out parametric fitting to obtain a current milling cutter position and a current milling cutter posture; inputting the initial tool setting path, the wear degree of the milling cutter, the current position of the milling cutter and the posture of the milling cutter into an error prediction model to predict the current motion error of the milling cutter; calculating the path compensation amount according to the current motion error of the milling cutter, and adjusting the tool setting path of the milling cutter according to the compensation amount. The method can reduce the interference of the motion blur of the milling cutter and environmental factors, and realizes the quantitative adjustment and correction of the tool setting of the milling cutter.

The invention provides a physical driving measurement method for monocular three-dimensional dynamic displacement of a rotary machine, and belongs to the technical field of crossing of computer vision and industrial state monitoring. According to the method, a high-speed dynamic visual acquisition system is constructed, and a time sequence video stream is obtained by using a cooperative marker; constructing a'Gaussian + motion blur 'composite gradient model taking the motion blur width as an endogenous variable, and jointly resolving sub-pixel edge coordinates and ambiguity by adopting a nonlinear optimization algorithm; decoupling the pixel displacement of the radial X axis, the radial Y axis and the axial Z axis by using the width change and the edge displacement of the marker in combination with the geometric principles of sequential robust filtering and monocular imaging; and finally, outputting a three-way physical vibration waveform by using the calibration conversion factor. According to the method, the fuzzy influence is adaptively eliminated from a physical imaging mechanism, micron-level precision three-direction vibration synchronous measurement is realized only by a single camera, and the hardware cost and the deployment difficulty are reduced.

The invention discloses an object level motion blur three-dimensional scene synthesis method and an object level motion blur three-dimensional scene synthesis device based on three-dimensional Gaussian splash (3D Gaussian splash, 3DGS). According to the method, a multi-view image and corresponding camera parameters are used as input, and three-dimensional Gaussian point scene representation including spatial position, scale, rotation, opacity and appearance characteristics is constructed; generating a binary mask of a moving foreground object and a static background by using an image segmentation model; on the basis, camera exposure time is dispersed into a plurality of time steps, space pose parameters changing along with time are introduced only for three-dimensional Gaussian points corresponding to the foreground object, instantaneous three-dimensional scene representation under each time step is obtained, integration or weighted averaging is carried out on rendering results of the plurality of time steps, and the real-time three-dimensional scene representation of the foreground object is obtained. Generating a motion blur rendering result with real physical significance; meanwhile, a mask-guided foreground-background separation rendering strategy and a partition loss function are adopted, clear rendering supervision is applied to a static background area, fuzzy rendering supervision is applied to a moving foreground area, and joint optimization of three-dimensional Gaussian scene parameters is achieved; in the reasoning stage, the clear background and the fuzzy foreground are fused and output according to the mask, and a three-dimensional scene reconstruction result with the clear background and the reasonable motion fuzzy effect of the foreground is obtained. According to the method, the exposure integral process and the object movement track are explicitly modeled in the three-dimensional geometric space, and object-level controllable fuzzy three-dimensional scene generation is realized.

The invention relates to the technical field of machine vision detection, particularly discloses a mechanical part surface defect vision detection system, and aims to solve the problems of blind areas, environment light sensitivity, insufficient tiny defect detection capability and the like in the detection of complex curved surface high-reflection parts. Comprising an optical imaging module, a geometrical morphology sensing module, a dynamic polarization regulation and control module, an image fusion and enhancement module and a defect identification and decision module. The three-dimensional morphology of the surface of the part is sensed in real time, the imaging polarization state is dynamically regulated and controlled according to the three-dimensional morphology, multi-frame polarization image fusion and digital diffraction enhancement processing are combined, full-coverage and high-contrast detection of the complex curved surface part is achieved, motion blur is effectively restrained, and the detection capacity of sub-pixel-level tiny defects and the system environment adaptability are improved.

The invention discloses a wheat seedling missing detection method for performing semi-supervised learning by fusing spatio-temporal information, and belongs to the technical field of seedling missing detection methods. The invention discloses a wheat seedling missing detection method for performing semi-supervised learning by fusing spatio-temporal information. According to the method, a set of semi-supervised learning framework is constructed; an initial model is trained by using a small number of precisely-labeled frames, and pseudo labels are generated for unlabeled frames; and'time sequence consistency 'and confidence coefficient are introduced to form a joint screening mechanism, and high-quality spatio-temporal data are obtained at low cost. In order to break through the limitation of single-frame detection, a space-time attention module is integrated in a detection network, and multi-frame information is aggregated to improve the robustness to motion blur and the like. The invention also provides a new normal form of space anomaly detection, which does not directly identify the missing seedlings, but learns a normal seedling space distribution rule, and identifies a low-density area as a seedling missing area by using a K-nearest neighbor (KNN) algorithm. According to the method, an unstable classification task is converted into a robust statistical problem, and the method has the advantages of low cost, high robustness and strong generalization.

The invention discloses an intelligent security and protection monitoring method, system and device and a storage medium, and the method comprises the steps: carrying out the cross-modal fusion analysis of a roughly extracted first visual feature and first radar feature of a target, and generating a dynamic fuzzy intensity coefficient based on an abnormal behavior result after the fusion analysis, so as to dynamically adjust the fuzzy intensity of a target face region; according to the change of the target dynamic fuzzy intensity coefficient, judging whether a high-risk early warning signal is output or not; if the high-risk early warning signal is output, cross-modal deep fusion analysis is carried out on the extracted fine second visual feature and second radar feature of the target, and a target abnormal behavior judgment result is output based on the features after deep fusion; and tracking the target based on the judgment result, associating a visual detection target with a radar tracking target when the target is visually shielded, predicting the future motion trajectory of the shielded target, and recovering visual tracking when the shielding is removed. Accurate detection of abnormal behaviors is realized.

The invention provides a scene reconstruction and automatic correction method using an unmanned aerial vehicle, and belongs to the technical field of scene reconstruction and correction, and the method comprises the steps: carrying out the global data collection of different sub-regions, synchronously recording the flight path information of each round, and carrying out the timestamp alignment processing of sensor data; constructing a dynamic error compensation model, and correcting motion blur, environmental interference and trajectory deviation errors in the collected data by combining a dynamic parameter set of random flight of each round of the unmanned aerial vehicle; integrating multiple rounds of data after correction, establishing spatial mapping of subarea textures and point clouds, and generating an initial three-dimensional model; and constructing a closed-loop correction mechanism based on the GNSS absolute coordinate of the unmanned aerial vehicle and the ground high-precision control point, calculating the deviation of the initial three-dimensional model, and if the deviation exceeds a threshold value, reversely adjusting the random flight coverage density, the flight round and the error compensation coefficient until the deviation reaches the standard. And the precision of scene reconstruction and correction is improved.

The invention discloses a writing process analysis method based on computer vision, which relates to the technical field of handwritten character recognition, and comprises the following steps: carrying out perspective transformation on real-time writing data, obtaining a writing image frame sequence, carrying out motion blur correction and pen point coordinate positioning on the writing image frame sequence by adopting an RAFT (Reversible Addition Fragmentation Transform) optical flow algorithm, and forming a writing track data flow; and performing curvature segmentation on the writing trace data stream to obtain discrete stroke segments, and performing spatial topological correlation and geometric structure mapping on the discrete stroke segments to generate a writing stroke topological graph. Through the RAFT optical flow algorithm and the stroke semantic analysis model, the precision of stroke recognition is improved, and efficient and accurate analysis from dynamic visual input to structured character output is achieved.

The invention discloses a tunnel face geological information acquisition and image processing method based on machine vision, which belongs to the technical field of image processing, and comprises the following steps: resolving the pose of a terminal in real time through a visual inertial odometer, generating augmented reality guide information, and guiding the terminal to move to a standard acquisition point; at each acquisition point, performing multi-stage verification of scene semantic compliance, motion blur and defocus and illumination uniformity on the image flow to ensure the acquisition quality; for tunnel high-dynamic illumination, adopting an adaptive brightness segmentation and multi-resolution fusion technology based on an improved Otsu algorithm to perform high-dynamic range reconstruction on a single-frame image so as to recover details; and finally, performing clustering optimization based on the feature matching similarity, and screening out an optimal image subset for three-dimensional reconstruction. According to the method, the problems of subjective and random acquisition process, many image quality defects, detail loss under extreme illumination and large image data redundancy are solved.

The present invention provides a system and method for utilizing multiple camera systems including at least three cameras, each camera having at least partly overlapping fields for removing blur in a moving object relative to a static background captured by the multiple camera system, comprising separating and extracting the moving object from the static background by isolating pixels corresponding to the moving object and distinguishing them from the static background by using images captured by the at least three cameras having field of views covering the moving object, enhancing clarity of the separated moving object by deblurring the separated moving, blurring the static background, reintegrating the deblurred separated moving object onto the blurred static background in the position of the images where the separated moving object was extracted.

The invention discloses a depth estimation method and device for any video and a storage medium, and belongs to the field of visual depth estimation. The method comprises the following steps: carrying out annotation processing on a scene video sample to obtain a deep annotation video data set; screening based on the depth labeling video data set and the TartanAir data set to obtain a spatio-temporal joint training sample; the method comprises the following steps: performing time sequence embedding on a multi-head attention layer in an encoder of a DepthAnything model to obtain a space-time combined multi-head attention layer, and constructing an initial TC-DepthAnything model; training the initial TC-DepthAnything model by adopting a space-time joint training sample, and performing constraint by adopting an overall training loss function formed by space consistency loss and time domain regularization loss in the training process to obtain a target TC-DepthAnything model; and inputting any video into the target TC-DepthAnything model to obtain a predicted depth video. The problem of time sequence jitter of DepthAnything in video depth estimation is solved, and flicker artifacts and motion blur in a dynamic scene are inhibited. And video depth estimation with a large application range and an accurate estimation result is realized.

The invention relates to the field of computer vision and mobile robot navigation, and discloses an object-level semantic vision SLAM method and system based on a mixed attention mechanism target detection network and an ellipsoid model. The system comprises five parallel thread modules, namely a semantic perception module, a visual tracking and repositioning module, a local mapping and fusion module, a loopback detection module and a global consistency optimization module. According to the method, local and global features of an image are extracted in parallel through a target detection network, and high-precision semantic observation is output; when the tracking is lost, the dual geometric constraint of the 2D internally tangent ellipsoid-3D object ellipsoid is utilized, and the P3P algorithm and the IoU cost function are matched to realize rapid relocation. In the mapping process, Gaussian-Wasserstein distance is introduced to measure semantic re-projection errors, and a joint objective function containing map point geometric errors and object semantic errors is constructed to carry out bundle adjustment. According to the method, the problem of feature extraction failure in motion blur and weak texture scenes is effectively solved, and the construction precision of the semantic map and the survivability of the system are remarkably improved.

The invention discloses a night enhanced imaging and dynamic denoising method for an automobile data recorder, relates to the technical field of vehicle-mounted image processing and computational photography, and is used for solving the problems of insufficient brightness and too strong noise of night videos of the automobile data recorder under a low-illumination condition. Estimating the zero-mean Gaussian noise intensity of a read link and the Poisson noise intensity increasing along with the brightness in the same frame, converting the zero-mean Gaussian noise intensity and the Poisson noise intensity into brightness equivalent parameters, and introducing a unified control function G as the scale reference of all enhancement and noise reduction links; defining a sharing priori H and carrying out unified constraint in dense optical flow, depth time sequence denoising and local tone mapping; and in combination with the motion mask and the background model, performing differential space-time denoising and proportional write-back, and in cooperation with motion blur detection, spatial variation motion kernel and constrained deconvolution and multi-frame detail recharge, obtaining target brightness and outputting an RGB frame after proportional write-back.

The invention discloses a two-stage cascaded video focus detection method and system, and relates to the technical field of image target recognition, and the method comprises the steps: obtaining an endoscope image; selecting a reference frame; carrying out iterative calculation on the feature embedding of the reference frame to obtain deep space-time representation; generating a time attention weight based on the deep spatio-temporal representation, and performing weighted fusion on the deep spatio-temporal representation to obtain an enhanced feature; performing dimension reduction on the enhanced features to obtain video prompt information; and extracting feature embedding of the target reasoning frame and reasoning frame features in the video prompt information, performing iterative calculation on the reasoning frame features to obtain deep reasoning features, and performing focus detection on the deep reasoning features to obtain a detection result. According to the method, a two-stage cascade Transform architecture is adopted, the quality degradation phenomena of dynamic blur, exposure imbalance, reflection artifacts and the like of inference frames are effectively relieved, and efficient joint modeling of spatial-temporal characteristics is achieved.

The invention provides an image visual identification processing method for foundation pit monitoring, and belongs to the technical field of data processing, and the method comprises the steps: 1, carrying out the multi-modal image and motion information collection of a foundation pit; 2, the background server carries out multi-modal feature fusion and blurred image screening on the foundation pit image; step 3, the background server performs rigid-non-rigid structure classification and feature enhancement based on the multi-modal feature map; 4, the background server performs dynamic fuzzy kernel construction and adaptive deblurring on the rigid structure area and the non-rigid structure area; and 5, based on the complete deblurred image, fine crack identification of the foundation pit is carried out. Through four core steps of multi-modal feature fusion extraction, dynamic fuzzy kernel construction, non-rigid structure enhancement and refined crack identification, efficient deblurring and accurate crack identification of a foundation pit image are realized.

The invention discloses a YOLO-based avalanche monitoring self-improvement system and method, and belongs to the technical field of geological disaster intelligent monitoring. According to the method, multi-source image data are acquired through an unmanned aerial vehicle and satellite remote sensing, and initial avalanche detection is realized through YOLO; screening high-confidence pseudo labels by using a time sequence consistency verification mechanism, and constructing an enhanced training set in combination with a dynamic time decay weight strategy; when pseudo labels are accumulated or the performance is reduced, the model is automatically triggered to be retrained, and a self-learning closed loop is formed through updating or rollback of a performance evaluation decision model. According to the method, YOLO detection is innovatively and deeply fused with automatic pseudo label distillation, physical parameter inversion and edge end spreading deduction, and full-chain self-improvement of detection-deduction-early warning-optimization is achieved. According to the method, the detection precision is continuously improved, the manual annotation amount is reduced, complex terrains and extreme environments such as strong light reflection and dynamic fuzzy are supported, and the early warning delay is reduced to a millisecond level.

The invention discloses a grayscale image translation motion blur recovery method and system based on optical flow guidance, and belongs to the technical field of image processing. A target area is determined based on a video stream H; calculating a translational motion vector field matrix between every two adjacent frames in the video stream H by adopting an optical flow method, obtaining a translational motion vector located in a target area, further constructing a track representation matrix for reflecting a motion track of a motion target generating translational motion, normalizing the track representation matrix to serve as a blurring kernel, and obtaining a motion vector field matrix; and translational motion blur of the target area is removed in a targeted manner. According to the method, the high-density time sampling information provided by the video stream H with the high frame rate and the low signal-to-noise ratio is utilized, and the complex translational motion track of the blurred image B with the low frame rate and the high signal-to-noise ratio in the exposure period can be accurately captured. The fuzzy kernel synthesized based on the accurate translational motion trails can highly approach a real physical fuzzy process, and an image with a high signal-to-noise ratio and high definition can be reconstructed.

The invention relates to the field of optical cable detection systems, in particular to a communication optical cable surface flaw detection system based on visual inspection, which comprises the following modules: a multi-modal data acquisition module, a data fusion processing module and a process linkage feedback module, according to the invention, through a multi-modal data acquisition architecture of ''event camera + micro X-ray + multi-spectral imaging'', the detection range is expanded from the traditional ''surface morphology'' to ''internal structure'' and ''material characteristic'' for the first time: the event camera can accurately capture micron-sized surface transient flaws under high-speed production, and the problem of motion blurring of the traditional camera is solved.

The invention provides an unmanned aerial vehicle aerial photography small target detection method based on a YOLO11 backbone network, a fusion feature enhancement module, a lightweight up-sampling module and a self-adaptive cross-layer fusion strategy, and aims to solve the problems of low detection recall rate, positioning drift and the like caused by small, dense and easy-to-shield targets, large scale change and motion blur in an aerial photography scene. And the small target detection precision and robustness are improved. The method specifically comprises the four main steps that firstly, an aerial photography data set is obtained, and an adaptive training data set is constructed through specification division, multi-scale zooming, Mosaic enhancement and input standardization preprocessing; inputting the preprocessed image into an improved backbone network, extracting enhanced features through a convolution and feature enhancement module, and optimizing a global receptive field in combination with a fast spatial pyramid pooling module and a cross-stage partial space attention module; then carrying out lightweight up-sampling and adaptive fusion to generate double 80 * 80 and single 40 * 40 resolution feature branches; and finally, outputting a target category and a bounding box through a detection head. The method provided by the invention can improve the small target detection performance of aerial photography of the unmanned aerial vehicle, and can provide technical support for scenes such as security and protection monitoring, electric power inspection, agricultural remote sensing and the like.

The invention relates to the technical field of machine vision, in particular to an intelligent sorting method and system for sundries on a conveying belt based on AI visual guidance, and the method comprises the steps: firstly, carrying out the deblurring of a to-be-detected image, eliminating the motion blurring, carrying out the image enhancement, enlarging the difference between the sundries and coal briquettes, and then achieving the precise recognition of the sundries through target detection, the impurity identification accuracy is improved from the source; then, image segmentation is carried out based on a sundry recognition result, then sundry geometric parameters and posture parameters are obtained, and an accurate target form basis is provided for grabbing; and finally, in combination with the speed of the conveying belt, the sundry detection position, the sundry maximum width, the sundry minimum width and the forward direction included angle, a motion path with synchronous time and adaptive posture is generated and converted into a control signal, a robot clamping jaw is controlled to clamp the sundry, and the problems of sundry recognition deviation, grabbing dislocation and response lag in a complex scene are effectively solved. The automatic sorting device is applied to automatic sorting of sundries on a conveying belt of a coal cleaning plant, and the sorting accuracy and efficiency can be improved.

The invention relates to the technical field of unmanned aerial vehicle image processing, and discloses an unmanned aerial vehicle image noise reduction and color enhancement optimization method and system. The method comprises the following steps: firstly, acquiring an original image sequence acquired in the flight process of the unmanned aerial vehicle, and identifying corresponding environment illumination intensity change data and motion fuzzy feature distribution; time dimension features of the original image sequence are divided based on environment illumination intensity change data, and a noise feature distribution map is constructed in combination with motion blur feature distribution; and finally, calling an equipment imaging parameter set of the original image sequence, analyzing a corresponding imaging mode constraint condition, and formulating a multi-stage noise reduction strategy framework for the original image sequence according to the constraint condition and the noise feature distribution map. The method realizes effective noise reduction and color enhancement of the unmanned aerial vehicle image by comprehensively considering the environment illumination change, the motion blur feature and the equipment imaging parameter, improves the image quality, and is suitable for the field of unmanned aerial vehicle image processing.