490 results about "Geometric transformation" patented technology

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention provides a geometric correction method and device for a satellite remote sensing image and a storage medium, and relates to the technical field of image correction, and the method comprises the steps: obtaining remote sensing image data of at least two different spectral bands, synchronously recording satellite attitude parameters and orbit position parameters, and carrying out the verification processing; based on the satellite attitude parameters and the orbit position, geometric coordinate conversion is carried out on each remote sensing image data, and target remote sensing image data is generated; dividing each piece of target remote sensing image data into a plurality of continuous grid units, and constructing a local geometric transformation model at the vertex of each grid unit; and resampling the original pixels in each grid unit according to the local geometric transformation model, and outputting a plurality of pieces of target remote sensing image data subjected to geometric correction. According to the invention, the geometric correction precision of the high-resolution satellite remote sensing image can be improved.

The invention discloses a pavement crack detection method based on a Yolov8 model, and belongs to the technical field of crack detection. Comprising the following steps: constructing a pavement crack segmentation data set: acquiring a road crack image through an unmanned aerial vehicle, performing data enhancement processing of geometric transformation and color transformation on the image in combination with a public data set, performing Gaussian filtering denoising on a noise image, and performing crack labeling by using Label; based on a YOLOv8-Seg model, improvement is carried out by introducing a PKIblock multi-scale convolution kernel, generalizing an efficient layer aggregation network GELAN module, an EMA attention mechanism and replacing a spatial pyramid pooling layer SPPF into a SimSPPF, and a road crack recognition model YOLOv8-RCI is constructed; and performing crack detection and instance segmentation on the unmanned aerial vehicle image by using the trained YOLOv8-RCI model, and outputting a crack position and mask information. Through the improvement in the four aspects, the detection segmentation performance of the model is improved, and the model meets the requirement of real-time detection.

The invention discloses an automobile part quality detection method and system based on artificial intelligence visual inspection, and belongs to the field of artificial intelligence machine visual inspection, and the method comprises the steps: firstly, carrying out the registration of a collected RGB image and a depth image, and extracting a part region through a saliency detection network; two-dimensional key points are extracted based on an RGB region image and are matched with key points of a three-dimensional model, an initial three-dimensional attitude is obtained by adopting a PnP algorithm, iterative registration is performed with the three-dimensional model in combination with a point cloud generated by a depth image, and a fine three-dimensional attitude is obtained. And calculating a geometric transformation matrix from the part to a standard front view attitude according to the attitude, and performing attitude correction on the RGB and depth region image. And then matching the corrected image with a standard template image by using a feature detection and matching network so as to correct the position of the detection window. And finally, the three-dimensional size of the part is calculated in the corrected detection window in combination with the depth value, and tolerance judgment is carried out. And the precision, the robustness and the automation level of online detection of the automobile parts can be obviously improved.

The invention provides a face image recognition method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the fusion of a topological relation feature vector and a biological feature contour, calculating the curvature manifold of the concave region of five sense organs, and generating a local curvature feature vector through differential geometric transformation; inputting the topological relation feature vector, the local curvature feature vector and the micro feature coding sequence into an evolvable biological feature library, and fusing a space-time weight to update a comparison template; fusing the micro-feature coding sequence, the topological relation feature vector and the local curvature feature vector to obtain a biological feature set, and calculating the multi-scale similarity between the biological feature set and the updated comparison template; and if the multi-scale similarity exceeds an adaptive threshold, generating a face recognition result. According to the invention, the stability and accuracy of face recognition in a complex illumination environment are improved.

The invention discloses a virtual fitting video generation method and system based on multi-view face fixation, and relates to the field of generative artificial intelligence and computer vision, and the virtual fitting video generation method based on explicit geometric constraints comprises the following steps: S1, obtaining an original fitting image and an action cue word, and constructing a multi-source input data set; s2, segmenting the original fitting image to obtain multi-view modeling, and generating a multi-angle face image and a clothing texture feature parameter; s3, analyzing the action cue word to generate a target posture sequence, and generating head and tail frame virtual fitting images; s4, performing pairing analysis on the virtual fitting images of the head frame and the tail frame to generate attitude transition parameters; s5, performing dynamic texture repair on the image to generate a transition frame image sequence; and S6, generating a virtual fitting video and outputting the virtual fitting video. According to the method, accurate segmentation and multi-angle modeling of the face area and the clothing area are realized through image segmentation and a rigid geometric transformation algorithm.

The invention belongs to the technical field of defect identification, and particularly relates to a gas turbine blade defect identification method based on an improved YOLOV8 network. Comprising the following steps: capturing a defect image; effective information is extracted from the image or subsequent target detection, classification and segmentation task requirements are met; performing data enhancement through geometric transformation and color perturbation; adding structured labels or annotations to the images, and endowing the images with semantic information; the scheme is improved on the basis of YOLOv8 so as to be realized in gas turbine blade defect detection, and training of the model is completed in a distributed heterogeneous computing framework by using an acquired training data set and an acquired verification data set and is used for a prediction task. The method can achieve the pixel-level detection and positioning of the defects of the blade, can effectively meet the daily detection demands, and gives consideration to the detection precision and real-time performance.

The invention discloses an unmanned aerial vehicle inspection image registration and alignment method based on deep learning, and belongs to the technical field of image processing. Comprising the steps of video acquisition and segmentation processing, semantic-space-time alignment and synchronization subsequence selection, content enhancement and alignment frame pair extraction, multi-source key point extraction and fusion, cross-video high-quality feature matching and geometric transformation estimation and registration, and solves the technical problem of high-precision frame alignment and matching in cross-view and asynchronous image sequences. The invention provides a semantic-spatio-temporal combined image sequence alignment method, which realizes high-robustness alignment of cross-sequence image frames, and enables matching to be more inclined to frame pairs in time proximity, thereby effectively inhibiting semantic interference of cross-time drift, enhancing physical rationality of a matching path, and improving matching accuracy. The robust alignment capability under asymmetric sampling, speed change and visual angle deviation in the real flight process is remarkably improved.

The invention discloses an isovariant consistency image fusion method and system based on task driving, and the method comprises the steps: carrying out the feature decomposition of an input infrared image and a visible image, obtaining the basic features and detail features, and carrying out the geometric transformation operation meeting the isovariant consistency constraint in the subsequent fusion processing, so as to guarantee the transformation stability of the features. And performing semantic constraint on the fusion model by utilizing supervision information of a semantic segmentation task of the semantic segmentation model. According to the method, the equal-variant consistency constraint and the semantic loss are collaboratively optimized through the loss function part, the robustness of geometric transformation can be kept, meanwhile, the reservation of local details and the enhancement of global semantic information are effectively considered, a high-quality fused image is generated, and the performance of a downstream advanced visual task is improved.

The invention provides a building identification method and system based on three-dimensional point cloud, and particularly relates to the technical field of computer vision and three-dimensional target identification, and the method comprises the steps: carrying out the undersampling, oversampling and data enhancement preprocessing of inputted building roof point cloud data, and carrying out the T-net network space alignment to output the aligned data. Then, an NEWPCT model is obtained by improving a PCT model, aligned data are input into the NEWPCT model, a point cloud feature matrix is generated through a linear coding layer, query, key and value matrixes are obtained through linear transformation, multi-head attention output is calculated by introducing an offset matrix, intermediate correlation features are obtained after splicing and fusion, and a global feature vector is generated through global maximum pooling; and finally, inputting the vector into a classifier, and outputting a building roof type, and the method solves the technical problems of geometric transformation sensitivity and local feature loss while improving the precision and robustness of three-dimensional point cloud building identification, thereby effectively improving the identification precision and robustness.

The invention discloses a real-time data stream processing method for multi-device collaborative management of a dome cinema, which relates to the technical field of cinema device data management, and comprises the following steps of: establishing communication connection between a central management node and a plurality of playing devices, collecting device identifiers, projection area numbers and geometric position parameters of the playing devices, and establishing a central management node; generating an equipment mapping relation table; realizing local clock correction of the playing equipment based on a unified time synchronization protocol; the central management node generates a control instruction stream according to the equipment mapping relation and the playing content, the instruction stream comprises space frame distribution information and fusion control parameters, and the space frame distribution information and the fusion control parameters are distributed to the playing equipment through communication channel numbers; and the playing device completes image geometric transformation and edge fusion processing according to the received control instruction and then executes synchronous playing. According to the invention, multi-device automatic mapping is realized, the time synchronization precision is improved, the image playing is coordinated and consistent, and the collaborative display capability and the playing stability of the dome cinema are improved.

The invention provides a brain tumor classification method based on deep learning, and the method comprises the steps: uniformly segmenting a multi-mode magnetic resonance imaging image into a plurality of small blocks, generating an enhanced puzzle through random geometric transformation, inputting the enhanced puzzle into a ResNet50 backbone network, extracting primary local features, and optimizing feature distribution; multi-scale feature maps of different stages are obtained, lightweight convolution blocks are constructed, and local pathological relevance is enhanced; performing full-connection layer splicing on the multi-scale feature maps in different stages, introducing a learnable weight matrix to dynamically allocate weights in each stage, and generating fusion features; a progressive parameter unfreezing mechanism is adopted to ensure orderly learning of the model from local to global; the sample weight is dynamically adjusted according to the category frequency, and the local discriminant force and the global consistency are balanced in combination with multi-stage classifier loss weighted fusion; and a final classification result is generated through multi-stage prediction probability weighted average and dynamic threshold adjustment, and clinical availability is improved in combination with a sigmoid calibration module.

The invention discloses a medical image semantic segmentation method based on attention mechanism optimization, and relates to the technical field of medical image processing, and the segmentation method comprises the specific steps: S100, data collection and label preprocessing: collecting medical image data from different medical institutions and a plurality of imaging devices, according to the method, the attention mechanism is introduced to carry out deep preprocessing on the medical image data, the precision and efficiency of semantic segmentation of the medical image are remarkably improved, the attention mechanism is utilized, key areas, such as diseased regions or tissue boundaries, in the image can be recognized and enhanced, meanwhile, noise and irrelevant information are effectively removed, and the accuracy of semantic segmentation of the medical image is improved. The refined preprocessing mode not only improves the quality of the image, but also provides a more accurate data basis for subsequent image detection and segmentation, and the method is also combined with a self-adaptive denoising algorithm, dynamic adjustment of contrast, brightness and color and a geometric transformation advanced preprocessing technology, so that the availability and diagnostic value of the image are enhanced.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention provides a multi-modal remote sensing image progressive registration method and system, and the method comprises the steps: 1, obtaining a pre-disaster and post-disaster multi-modal remote sensing image pair of a disaster region, constructing a disaster scene multi-modal disaster remote sensing image registration data set, and dividing a training set and a test set; step 2, constructing a multi-scale depth feature extraction network, performing depth coding on the multi-modal remote sensing image pair, respectively extracting local details and global structure features, and generating a multi-scale feature pyramid; 3, constructing a progressive cross-modal transformation network based on the multi-scale feature pyramid, mining modal invariant features through a cross-modal attention mechanism, and regressing a multi-scale symmetric dense deformation field pixel by pixel; and 4, based on the multi-scale symmetric dense deformation field, training a deep learning model through a consistency loss function, performing geometric transformation on test set data by using the trained deep learning model, and outputting a registration result. According to the invention, automatic geometric correction of the multi-modal remote sensing image is realized.

The invention relates to the technical field of image processing, in particular to a rubber ring contour burr detection method and system based on geometric transformation, and the method comprises the steps: obtaining a rubber ring image, and extracting the outer contour of a rubber ring and the contour of each burr in the rubber ring image; fitting the outer contour of the rubber ring by adopting a least square method to obtain an ellipse, and mapping the ellipse into a standard circle: carrying out bilinear interpolation resampling on the rubber ring image, and carrying out polar coordinate transformation on the rubber ring image by taking the circle center of the standard circle as an original point to generate an expanded image; generating a reconstructed image with enhanced burr features from the expanded image through wavelet transform, determining burr parameters of the reconstructed image, mapping the burr parameters back to a Cartesian coordinate system of the rubber ring image, and generating a detection result containing burr number, position and size information; according to the invention, the accuracy and robustness of tiny burr detection can be improved.

The invention relates to a GIS (Gas Insulated Switchgear) equipment mechanical defect diagnosis method based on a Gramb angle field and a dual-channel PCNN-Attention neural network, and belongs to the technical field of gas insulated switchgear mechanical vibration defect diagnosis. The method solves the problems that traditional diagnosis depends on artificial feature extraction, so that subjectivity is high, information mining is insufficient, and defect severity evaluation is missing. According to the technical scheme, the method comprises the steps that a one-dimensional vibration signal is converted into a GASF two-dimensional image and a GADF two-dimensional image through a GASF field so as to completely reserve time sequence topological features; carrying out data enhancement by adopting an image geometric transformation technology so as to improve the generalization ability of the model; and a dual-channel PCNN-Attention model is constructed, and synchronous intelligent identification of defect types and severity is realized through parallel feature extraction and dynamic weight optimization of an attention mechanism. According to the method, the diagnosis accuracy, reliability and adaptive capacity are improved, and support is provided for equipment state operation and maintenance.

The invention discloses an airborne visible light image automatic splicing method, and relates to the field of unmanned aerial vehicle image processing, and the method comprises the steps: obtaining a plurality of images collected in the flight process of an unmanned aerial vehicle, and the corresponding spatial position information and attitude information; on the basis of the spatial position relation between the images and the image overlapping information, determining image pairs capable of being registered, and constructing a connection map with the images as nodes and the image pairs capable of being registered as edges; detecting whether spatial connection fracture caused by image missing exists in the atlas or not, if so, inserting a virtual node and constructing a virtual image comprising an edge region and a transition region; and further calculating geometric transformation parameters between the images, resampling all image contents to a unified coordinate system, and executing pixel-level fusion processing in an image overlapping region. According to the invention, the problem of discontinuous image splicing of the unmanned aerial vehicle due to the existence of a no-fly zone, a privacy protection zone and the like of the unmanned aerial vehicle is solved.

The invention discloses a video source identification method combining PRNU matching and inter-frame geometric transformation estimation, and relates to the technical field of video source identification, and the method comprises the following steps: S1, photosensitive response non-uniformity extraction; s2, performing three-dimensional geometric transformation; s3, performing video source identification in combination with photosensitive response non-uniformity matching and inter-frame geometric transformation estimation; s4, fitting geometric transformation parameters; and S5, modeling and visualizing a shooting behavior track. The technical problem to be solved by the invention is to provide a video source identification method combining PRNU matching and inter-frame geometric transformation estimation, inter-frame alignment of a reference fingerprint and a test fingerprint is carried out through a multi-scale transformation method, a PRNU matching model is constructed by using deep learning, and a correlation score of a test video frame and a reference video frame is calculated. And the optimal geometric parameters are fitted by using an inter-frame geometric transformation algorithm, the shooting behavior is restored, and the matching accuracy and efficiency are improved.

The invention relates to the field of projection image calibration, in particular to a self-adaptive calibration method and device of a projection touch system. The method comprises the following steps: identifying pre-projection image information, carrying out projection area layout design, and outputting a pattern layout projection effect; performing pattern positioning calculation on the pattern layout projection effect one by one, and generating a sub-pixel positioning coordinate of each pattern; calculating a central point of a projection area according to the sub-pixel positioning coordinates, and constructing an actual projection positioning coordinate system; theoretical position deviation calculation is carried out according to the actual projection positioning coordinate system, and projection pixel point position deviation of each pattern is extracted; and performing multi-stage progressive pattern calibration compensation based on the projection pixel point position deviation to obtain a geometric transformation compensation result. The pattern position precision of the projection touch control image is improved, and the visual effect and precision of the projection pattern are optimized.

The invention discloses an infrared small target segmentation method and device based on a gating unit and a multi-scale convolutional network, and belongs to the technical field of image processing. The method comprises the following steps: acquiring and preprocessing infrared image data, and performing data enhancement through geometric transformation, radiation transformation and elastic transformation; a multi-scale convolutional network based on a gating unit is constructed, an encoder adopts pyramid vision Transformer (PVTv2) to extract four-level multi-scale features, cross-level connection realizes cross-layer feature adaptive propagation through the gating unit, and a decoder acquires the multi-scale features through multi-scale parallel convolution to enhance the small target feature expression ability; a deep supervision mechanism is adopted, prediction results of all levels are fused through self-adaptive weight, and the network is optimized through the weighted sum of binary cross entropy and confidence loss. The device comprises an infrared sensor, a preprocessing module, a processor for loading the model and an output module. According to the method, through multi-scale feature dynamic fusion and a deep supervision strategy, the segmentation precision and robustness of the infrared small target under a complex background are effectively improved, background interference is suppressed, and the method is suitable for the real-time segmentation requirement of a low-signal-to-noise-ratio scene.

The invention relates to the technical field of medical instruments, and discloses a tracheotomy anatomical structure image recognition method and system, and the method comprises the steps: obtaining real-time visual image data and spatial positioning data of an endoscope, carrying out the time synchronization, carrying out the mode recognition of the spatial positioning data, and locking the remarkable anatomical features in the real-time visual image data, and according to the characteristics and the spatial positioning data, calculating a geometric transformation relationship between an endoscope visual coordinate system and a spatial positioning coordinate system, and finally fusing the data and displaying anatomical structure information and a surgical tool position in real time. The method can effectively solve the problem that in the prior art, an endoscope is irregularly deformed in the narrow trachea with physiological bending of a patient.

The invention provides a video processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed video, and splitting the to-be-processed video into a plurality of video frames to obtain a video frame sequence; aiming at each video frame in the video frame sequence, detecting flower character areas at four corners of the video frame to obtain flower character area data containing flower character area position information; analyzing and processing the flower character region data of all the video frames to obtain a target flower character region set meeting a preset condition; performing comparison calculation on the basis of the size information of each target flower character region in the target flower character region set and a standard proportion to obtain a video proportion correction parameter; and performing geometric transformation processing on the video frame sequence according to the video proportion correction parameter to obtain a corrected target video. The deformation or loss of the patterns caused by resolution adjustment is avoided, and the time bottleneck of manual frame-by-frame operation is eliminated through a batch processing mechanism.

The invention discloses a real-time remote image rendering method and system based on multi-level-of-detail proxy, and relates to the technical field of real-time rendering. The method comprises the following steps: acquiring multiple reference views of an image to be rendered, capturing geometric information and material information from the multiple reference views, and constructing a hierarchical agent structure; projecting agent data in the hierarchical agent structure to a target view through a forward mapping mode based on geometric transformation, and retaining most effective data through a shallowest depth pruning strategy; carrying out dual-stage missing region repair on a forward mapping result by combining bilateral filtering and thin structure region detection; and performing dynamic and editable re-coloring processing based on the geometric information and the material information in the repaired target view. According to the method, efficient and high-quality real-time rendering is realized through compact geometry and material information coding, dynamic re-coloring support and multi-level detail management.

The invention relates to the technical field of image updating, in particular to a multi-modal image real-time updating method and system for temporal bone surgery. The method comprises the following steps: acquiring a real-time multi-modal image, performing pixel-by-pixel frequency domain reconstruction optimization, and constructing a frequency spectrum enhanced fusion image; performing reverse geometric transformation compensation on the spectrum enhancement fusion image to obtain a space alignment optimization image; performing real-time visual contrast enhancement on the space alignment optimization image, and constructing a visual enhancement image; carrying out inter-frame difference calculation on the vision enhanced image, carrying out real-time increment updating optimization, and constructing an increment updating image sequence; and performing multi-level cache rendering management and parallel execution based on the incremental updating image sequence. The temporal bone surgery safety and efficiency are improved through real-time and efficient image updating.

The invention relates to an unmanned aerial vehicle indoor three-dimensional reconstruction autonomous acquisition method and system based on reinforcement learning, and the method comprises the following steps: constructing a virtual indoor simulation space and a virtual unmanned aerial vehicle model, and laying target acquisition points; a reinforcement learning model is constructed and trained, in the training process, in each time step, the unmanned aerial vehicle obtains target collection point information and inputs the target collection point information into the reinforcement learning model, the reinforcement learning model generates an action instruction and transmits the action instruction to the virtual unmanned aerial vehicle model, and the virtual unmanned aerial vehicle executes geometric transformation, updates the position and orientation and reconstructs view information; and deploying the trained reinforcement learning model in an untrained virtual simulation environment, carrying out an unmanned aerial vehicle autonomous flight experiment and data acquisition, and carrying out quantitative evaluation on the acquisition performance through a plurality of performance indexes. Compared with the prior art, efficient and stable data acquisition can be realized in a complex environment.

The invention relates to the technical field of Internet television services, in particular to an OTT visual feature extraction system and method based on multi-modal agent driving, and the method comprises the steps: capturing a screen real-time video stream of equipment; processing the target advertisement image and the real-time video stream, and extracting double-flow heterogeneous visual features, including global content perception features and local geometric structure features, through a multi-modal visual perception model; executing a hierarchical matching algorithm, calculating and screening out candidate frames by using global content perception features, matching in the candidate frames by using local geometric structure features to establish a corresponding relation set containing all matched initial key points, performing spatial clustering on the set to separate out advertisement instances, and obtaining bounding boxes of the instances through geometric transformation calculation; and according to the bounding box, performing highlight display on the area where the target advertisement is located on the original video frame to generate a visual broadcast monitoring result. According to the invention, through multi-mode intelligent body driving, OTT advertisement visual feature extraction and broadcast monitoring are realized.

The invention relates to the technical field of distribution line hidden danger diagnosis, in particular to a distribution line hidden danger diagnosis system and method fusing visible light and infrared images, and the method comprises the steps: synchronously obtaining a visible light image and an infrared thermal image of a target area of a distribution line, and carrying out the geometric transformation of the infrared thermal image, so as to generate a registered infrared image; the visible light image and the registered infrared image serve as double-path input and are sent into a deep fusion network model composed of an encoder-decoder structure, and a fusion feature image is generated; inputting the fused feature image into a Bayesian neural network model, and calculating to obtain a first coupling output and a second coupling output; and comparing the second coupling output with a preset risk threshold to obtain a distribution line hidden danger diagnosis result. According to the invention, by quantifying the uncertainty of the diagnosis result, the intelligent diagnosis of the reliability is realized, and the robustness and the intelligent level of the diagnosis system are remarkably improved.

The invention relates to a method of determining one or more conditions, of a geographical line, GL, or its surroundings. The method comprises: acquiring geo-referenced earth observation data representing one or more spatially overlapping imagery layers covering from an aerial or space perspective a specific region of interest, ROI, of the Earth's surface, the ROI comprising the GL; geometrically transforming the geo-referenced observation data, at least in parts, into a local internal frame of reference of the GL within the ROI to obtain a mapping of the geo-referenced observation data to respective corresponding coordinates within the local internal frame of reference of the GL; and evaluating the mapped earth observation data as represented in the local internal frame of reference of the GL according to a classification scheme to obtain therefrom evaluation data representing a classification of one or more properties of the GL or of its surroundings according to one or more conditions of the GL.

The invention discloses a photovoltaic cell edge grabbing positioning processing method, which comprises the following steps: a, a distortion correction step: carrying out distortion correction on a camera by using a calibration plate, and generating a position mapping table which is used for recording a corresponding relation between pixel coordinates in a camera view range and world coordinates of the calibration plate; b, an image acquisition and edge extraction step: acquiring an image of the photovoltaic cell, and performing sub-pixel-level edge extraction on the image to obtain pixel point data of the edge of the cell; c, a distortion correction processing step: carrying out distortion correction processing on the edge pixel points based on the position mapping table to obtain corrected edge data; according to the edge-grabbing positioning processing method for the photovoltaic cell, high-precision positioning processing of the photovoltaic cell is realized by using a low-cost wide-angle camera and combining high-precision image processing and geometric transformation technologies.