119 results about "Visual segmentation" patented technology

The invention discloses a multi-crack width quantification method and system based on machine vision and a local unit method, and relates to the technical field of civil engineering structure crack detection, and the method comprises the steps: processing a crack image of a civil engineering structure through a vision segmentation model, and carrying out the automatic division through a connected domain marking function, identifying a connected domain of each crack; in combination with a connected domain analysis method, generating an external rectangular frame of the crack region; dividing an external rectangular frame of each crack area into a plurality of local units; calculating center point coordinates of all crack pixels, and performing linear fitting on all crack pixels in each local unit; based on an edge detection and corner detection algorithm, extracting edge pixel coordinates and corner coordinates of the crack, and performing linear fitting to obtain an edge fitting straight line; and the crack width of each local unit is determined, and the complete crack width parameter of each crack is determined. According to the invention, the technical problem of low quantification precision of the crack width in the prior art is solved.

The invention relates to the technical field of image analysis, in particular to a galvanized pipe surface defect detection method and system, and the method comprises the following steps: S1, carrying out the visual segmentation processing of a galvanized pipe surface image, carrying out the frequency distribution and phase information extraction of texture features of each region, obtaining the color histogram value of a local region, and carrying out the recognition of the color histogram value; and carrying out normalization processing on the color histogram and the extracted texture features, and establishing a local texture and color feature data set. According to the invention, through visual segmentation of the surface image of the galvanized pipe and extraction of the frequency distribution and phase information of the texture features, the precision of capturing the complex texture on the surface of the galvanized pipe is improved. In the clustering process of the local feature data, by setting the clustering center and marking the anomaly of density difference and texture abrupt change, the area with the surface density obviously deviated or the texture complexity abrupt change can be effectively identified, and the positioning precision of the abnormal area is improved.

The invention discloses a dexterous grabbing method and system for a robot, and aims to solve the technical problems that an existing robot grabbing system is high in dependence on hand-eye calibration precision (especially translation vectors), and the grabbing capacity of objects without priori knowledge is limited. According to the method, a reference-transformation-correction control framework is provided, the pose of an unknown object is accurately recognized through a multi-stage visual segmentation strategy, and a preliminary grabbing target is generated based on the reference relation of one-time teaching; most importantly, the system starts closed-loop visual servo correction, and iteratively corrects the position by detecting the real-time pose of a tail end visual mark, so that translation errors in hand-eye calibration are eliminated, and high-precision alignment is realized; in addition, the system also integrates the functions of object posture adjustment and rapid rotation calibration. Through cooperative work of open-loop target generation and closed-loop position correction, dependence on hardware accurate calibration is effectively reduced, and the grabbing success rate and robustness are improved.

The invention discloses an offshore remote sensing ship image segmentation method based on a visual segmentation large model, and the method comprises the following steps: obtaining a plurality of to-be-detected offshore remote sensing ship images, and taking the images as a to-be-detected data set; obtaining a visual segmentation large model, wherein the visual segmentation large model comprises an image encoder, a prompt encoder and a mask decoder; replacing a plurality of attention network layers of an image encoder of the obtained visual segmentation large model by adopting a low-rank adapter network; training the replaced visual segmentation large model to obtain a trained marine remote sensing ship image segmentation large model; and inputting a to-be-detected marine remote sensing ship image into the trained marine remote sensing ship image segmentation large model, and outputting a semantic segmentation prediction map. According to the method, ship semantic segmentation can be rapidly and accurately carried out under the complex background of the marine remote sensing ship image, and the efficiency and accuracy of marine remote sensing ship image segmentation are improved.

The invention discloses an audio-visual segmentation method integrated with frequency domain design. Firstly, a method for exploring comprehensive audio-visual alignment and fusion by combining a spatial domain and a frequency domain is introduced, audio-visual consistency is improved by integrating frequency domain information, and fine alignment of multi-modal features is enhanced. A frequency domain-oriented audio integration module and a frequency domain-based cross-modal fusion module are provided. Wherein the frequency domain oriented audio integration module encodes audio information into visual representation through early fusion based on frequency domain enhancement, so that more detailed visual representation of audio perception is generated, and subsequent generation of more robust multi-mode representation is facilitated; the cross-modal fusion module based on the frequency domain aims at exploring audio-visual association in combination with the spatial domain and the frequency domain and enhancing cross-modal feature alignment, so that the segmentation performance of the model is improved. The frequency-domain-oriented audio integration module is integrated to each stage of the encoder, frequency-domain information and audio clues are fully utilized, the difference between audio-visual modes is reduced, and more accurate audio-visual fusion and alignment are facilitated.

The invention discloses an audio-visual segmentation method based on time sequence cross-modal design Mama, and the method comprises the steps: carrying out the feature extraction of an input video and audio sequence through a visual and audio encoder, and obtaining multi-scale visual and audio features; wherein the visual features of the second level to the fourth level are processed by a multi-scale time sequence encoder to obtain enhanced visual features; the enhanced visual features and the original audio features are sent to a modal aggregation decoder, and intra-frame and inter-frame visual-audio cross-modal fusion is performed through a visual-audio selective scanning module to obtain a fused audio query; sending the highest resolution feature map of the first level, the enhanced visual features and the original audio features into a context fusion pyramid, and performing cross-frame feature enhancement and up-sampling through a time sequence Mamba module and an audio-visual selective scanning module to generate optimized mask features; and the final segmentation mask is obtained by matrix multiplication of the mask features and the fused audio query.

The invention discloses a long-distance rail identification and obstacle detection method and system, and the method comprises the steps: combining a long-focus optical system with an ultra-high-speed industrial camera at a hardware level, and building a high-collection-frame-rate and long-distance optical detection system; on the software level, a region of interest for obstacle detection is delimited according to recognition and visual segmentation of left and right rail lines of a rail, advanced visual tasks such as a rail obstacle intrusion detection algorithm based on target detection are executed, and finally high-frame-rate recognition and detection of long-distance rails and rail obstacles are achieved. According to the invention, a Schmidt-Cassegrain optical system with a focal length of 2000mm is used, and an ultra-high-speed industrial camera with a resolution of 1920 * 1080 and 3000 frames per second is combined to build a long-distance high-frame-rate optical detection platform which is erected on a driving position of a train to detect railway conditions within a range of 6km in real time; serious accidents caused by overlong braking distance due to high-speed running of the train can be prevented. Compared with a traditional train driver eye observation and laser radar-based scanning imaging mode, a farther detection distance, better detection and recognition robustness and a higher frame rate can be realized through the long-distance optical observation platform and the rear-end real-time detection system.

The invention discloses a colorectal cancer pathological image semi-supervised semantic segmentation method for regulating and controlling a pseudo label, and relates to the field of medical image visual segmentation. The method comprises the following steps: firstly, performing image acquisition and marking on a target pathological region, and dividing the target pathological region into a training set, a verification set and a test set in proportion; in the consistency regularization process of training the teacher model, sorting and reducing the categories of the low-confidence pseudo labels according to the confidence scores; the confidence coefficient of the pseudo-labels is evaluated through the prediction score difference between the final model and the optimal model of the trained teacher network, and the pseudo-labels with the high confidence coefficient are refined; according to the number of categories in the pseudo tag, setting the triggering probability of each sub-data enhancement method, and generating cascaded pixel random change; and finally, constructing a semi-supervised semantic segmentation model for the colorectal cancer pathological image false label. The problem that the semi-supervised semantic segmentation task of the colorectal cancer pathological image is low in precision is solved.

The invention discloses a semantic map construction method based on an RGBD visual segmentation algorithm, and the method comprises the steps: firstly initializing each parameter, constructing a visual segmentation neural network model, carrying out the visual semantic segmentation of a collected image, carrying out the calculation of the collected image, converting a depth image into point cloud data, carrying out the sparsification processing, and carrying out the segmentation of a visual segmentation neural network model; and then generating point cloud data with semantic information, superposing the point cloud data with external parameters of the camera, projecting the superposed point cloud data to an actual three-dimensional space, determining a spatial grid with semantic information corresponding to each moment, superposing and aging the acquired spatial grids at different moments, and constructing a complete semantic map based on data acquired in a time sequence. According to the scheme, the requirement for equipment is low, the detection range is wide, the requirement for computing power is low, robustness is high, dense sensing of the surrounding space can be achieved even if the constructed semantic map is sparse and matched with a proper frame rate, and the very high fine degree can be achieved.

The invention relates to a crop plot identification method and system based on time sequence vegetation characteristics, and the method comprises the steps: constructing a normalized difference vegetation index time sequence according to a multi-temporal satellite remote sensing image, and generating a crop probability distribution diagram through a crop classification model; performing connected domain analysis on the crop probability distribution map, extracting the center of gravity of an effective connected domain as a forward attention point, scanning the crop probability distribution map by using a sliding window, and generating a reverse attention point; based on the forward attention point and the reverse attention point, segmenting the high-resolution remote sensing base map through a visual segmentation model, generating a candidate mask set, and screening to reserve a mask with the maximum area as a land parcel segmentation mask of the forward attention point; and combining all the plot segmentation masks to generate a crop plot identification graph, and converting the boundary of each plot segmentation mask into a geodetic coordinate sequence to obtain plot vector boundary data. The method improves the recognition accuracy, and achieves the automatic and precise segmentation of the boundary of the land parcel.

The invention relates to the technical field of artificial intelligence and multimedia, in particular to an audio guidance visual segmentation method and device based on multi-granularity cross-modal coupling, and the method comprises the steps: extracting multi-level visual features and audio Mel spectrum features of a target video frame; performing intra-modal enhancement on the multi-level visual features and the audio Mel spectrum features to obtain enhanced multi-level visual features and enhanced audio Mel spectrum features; performing cross-modal fusion on the enhanced multi-level visual features and the enhanced audio Mel spectrum features to generate a semantic enhancement query vector; and training a pre-constructed Transform attention decoder by using the semantic enhancement query vector to generate a pixel-level segmentation mask, and fusing the pixel-level segmentation mask with the multi-level visual features to obtain a mask prediction result. Therefore, the problems of intra-modal noise interference, insufficient audio guidance, multi-source sound entanglement and the like in an existing audio-visual segmentation method are solved.

The invention discloses an unsupervised audio-visual segmentation method and system and electronic equipment, and the method comprises the steps: obtaining an audio-visual data set, and carrying out the extraction to obtain audio features and visual features; performing feature mapping on the features of the audio mode and the visual mode to a public feature space, calculating feature association of the two modes through cross attention, and determining coarse-grained positioning of a video frame on a sound source target; mining neighborhood semantic association of the visual features, constructing a semantic similar matrix, and performing fine-grained correction on sound source target segmentation to obtain a first sound source target segmentation map; generating a second sound source target segmentation image through the coding and decoding network according to audio and visual feature interactive learning; and aligning the first sound source target segmentation image and the second sound source target segmentation image to obtain an optimized final audio-visual segmentation result. According to the embodiment of the invention, the boundary information of the sound source target can be more accurate, the sound source target segmentation map can be generated more stably and accurately, and the method can be widely applied to the technical field of audio and video processing.

The invention discloses a three-dimensional medical image segmentation method based on a prompt driving mechanism, and the training process of an image segmentation model adopted by the three-dimensional medical image segmentation method comprises the steps: obtaining a data set, carrying out the preprocessing operation of a label sample in the data set, and enabling the label sample to comprise a training set and a test set, the data set comprises a medical volume image and a volume segmentation image; performing three-dimensional adaptation and hierarchical strategy improvement on each module in the visual segmentation basic model, realizing modification of an image encoder and a prompt encoder in the visual segmentation basic model, and obtaining a training model for prompt embedding and image embedding alignment; and training the training model based on the training set by adopting an iterative training strategy of simulation interaction, and evaluating and storing the model with the optimal evaluation index based on the test set to obtain an image segmentation model. Through the image segmentation model based on the prompt driving mechanism, the overall segmentation precision of the medical image can be improved.

The invention provides a multi-mode large model robot control method and device, equipment and a medium, and the method comprises the steps: carrying out the feature extraction, normalization processing and cross attention processing of a plurality of RGB images and depth images of a robot in a robot control model, and determining visual features; performing feature processing on the joint torque signal of the mechanical arm and the real-time current signal of the tail end gripper to determine a force feedback feature of the robot; feature coding processing is conducted on the task instruction of the robot, the current mechanical arm pose information and the current mechanical arm motion frequency, and task instruction features, visual segmentation features, pose features and frequency features are determined; and performing interactive attention processing and causal attention processing on the visual features, the force feedback features, the task instruction features, the visual segmentation features, the pose features and the frequency features in a robot control model, and outputting an action decision and visual information of the next moment. Therefore, the accuracy of action decision is improved.

The invention relates to the field of reagent strip packaging quality detection, in particular to an in-vitro diagnostic reagent strip packaging quality control method and system. The method comprises the following steps: acquiring a packaged in-vitro diagnostic reagent monitoring image; performing multi-scale reconstruction on the in-vitro diagnostic reagent monitoring image so as to generate a multi-scale noise reduction reconstructed monitoring image; performing image visual segmentation and image iteration packaging defect detection on the multi-scale noise reduction reconstruction monitoring image one by one so as to obtain a packaging defect part expansion monitoring image; carrying out surface defect texture mining on the packaging defect part expansion monitoring image, and carrying out deep defect factor inference so as to generate packaging defect inference factors; obtaining a reagent strip packaging log; and defect process tracing is performed on the reagent strip packaging log according to the packaging defect inference factors, so that packaging process stages causing defects are marked. The packaging parameters are automatically adjusted through real-time feedback, so that the packaging quality of the in-vitro diagnostic reagent strip is accurately improved.

The invention relates to the technical field of construction safety monitoring, and particularly discloses a construction equipment situation analysis and event identification method and system based on visual segmentation. The method comprises the following steps: acquiring a construction equipment operation video stream through a roadside camera, and constructing an image and physical space mapping relation based on scene ground key feature points; a YOLOv11-Seg instance segmentation network is adopted to identify the contour of the equipment and a landing part, and a cross-frame motion track is tracked through a YOLOv11-Track algorithm; calculating a safe operation boundary by combining equipment geometric parameters, landing point coordinates and a space mapping relation; detecting personnel equipment invasion behaviors in real time, and implementing dynamic early warning according to kinematics parameters; and identifying operation event modes and anomalies through spatio-temporal feature analysis. According to the invention, high-precision perception of the operation situation of the construction equipment can be realized, the dynamic safety boundary model early warns the construction risk in real time, and spatial-temporal feature analysis provides a basis for intelligent management and efficiency improvement of the construction site.

The invention discloses an audio-visual segmentation method based on multimode understanding, and the method specifically comprises the steps: firstly, extracting video image features and audio features through a visual backbone network and an audio backbone network respectively; performing deep analysis on the visual features by using a visual encoder to obtain deeper visual information; next, fusing visual and audio features by using an audio-visual mixing module, and reinforcing the features of the target object by means of audio information; finally, a multi-mode decoder is utilized to match queries and optimal features, and a final segmentation mask is generated. According to the invention, the understanding of the multi-modal semantic information is enhanced through the double-tower structure model, the mutual supervision and enhancement of the modals are realized by means of the audio-visual mixing module, and the audio-visual segmentation with higher performance is realized.

The invention relates to the technical field of machine vision, in particular to an industrial equipment operation analysis method and system based on machine vision. According to the method, the track information of the guide rail of the numerical control machine tool is obtained, and the three-dimensional coordinates and the timestamp sequence of the track points are established, so that the dynamic change of the track of the guide rail can be accurately described, the key track points are extracted through density analysis, and it is ensured that the perception of the track form change is more targeted. The time window division of the trajectory data is combined with the form change amplitude calculation, so that the quantification of the trajectory deviation trend is realized, and the trajectory anomaly can be quickly identified. In combination with stress analysis of a track deviation overrun area, an expansion path of fatigue damage can be determined, and a visual description of a damage propagation process is formed. Through extraction and analysis of a guide rail surface visual image, features of local cracks, wear and deformation can be matched with track anomalies, a visual segmentation boundary is further optimized, and it is ensured that details of structure changes are accurately extracted.

The invention provides a semi-supervised image segmentation method based on adaptive pixel subdivision, and relates to the technical field of computer vision and machine learning, and the method comprises the steps: firstly collecting image data according to a target scene, carrying out the preprocessing, dividing a training set and a test set, and marking part of data; preliminary segmentation is carried out by using a preset semantic segmentation model, a key pixel point region is screened through uncertainty analysis, and the segmentation precision is improved in combination with multi-scale features and iterative optimization. A teacher-student model framework is constructed, a teacher model is utilized to generate a pseudo tag, a high-confidence sample is screened to optimize a student model, and a contrast learning strategy is introduced to low-confidence pixels to enhance the generalization ability of the model. And finally, inputting the to-be-segmented image into the optimized student model, outputting a category matrix and generating a visual segmentation result, thereby improving the segmentation precision and boundary detail performance, and being suitable for a scene with limited annotation data.

The invention relates to a ridge line navigation method and system based on visual segmentation. The ridge line navigation method and system based on visual segmentation comprises the following steps: acquiring an original image, preprocessing the original image, and importing a YOLOv8 model for training to obtain a ridge surface segmentation mask; edge detection is carried out on the ridge surface segmentation mask, pixel point information of ridge surface contour coordinates is determined, and central point pixel coordinates are obtained; fitting navigation lines for all the center points by adopting a B spline interpolation method, and drawing reference lines; and calculating the average absolute percentage error of each pixel point of the reference line and the navigation line, confirming a deviation signal, and judging the vehicle body adjustment direction through the deviation signal. According to the scheme provided by the invention, the model can be trained through the complex environment image, the ridge surface segmentation mask can be obtained through the YOLOv8 model, the field ridge contour boundary can be extracted, the navigation line can be fitted by adopting the B-spline interpolation method, the real-time reference line can be set, the average absolute error of the navigation line and the reference line can be calculated, the deflection signal can be given, and more accurate deflection information can be provided.

The invention discloses a historical and cultural block building point cloud monomer segmentation method, system, device and medium, and the method comprises the steps: firstly collecting the building point cloud data of a historical and cultural block, and then calculating the local geometric features and scene features; and taking the local geometric features and the scene features as inputs of a semantic classification model based on a self-attention mechanism, extracting building point clouds, and processing the extracted building point clouds by adopting a normal-based region growing algorithm to obtain building edge point clouds. The method comprises the steps of extracting building point clouds and building edge point clouds, performing cross-domain rasterization processing on the extracted building point clouds and building edge point clouds, enhancing building structure edges, generating a building edge information enhanced image in an overlook view angle, and finally segmenting the building edge information enhanced image by adopting a visual segmentation model to obtain a building single point cloud segmentation result. By adopting the method, a more accurate monomer segmentation result can be obtained in dense areas such as buildings in historical and cultural blocks.

The invention provides a friction stir welding defect detection method fused with weak supervised learning, which belongs to the technical field of welding defect detection, and constructs a unified detection framework suitable for various weak labels such as points, frames, graffiti and the like by referring to the advantages of a large model SAM (Section Anything Model) in visual segmentation. The method comprises the following steps: firstly, converting different types of weak tags into an input format acceptable by SAM by adopting a prompt adapter module, and enhancing the prompt compatibility of a model; and secondly, abnormal region responses are eliminated through a response filter, and the recognition precision of the disguise or low-contrast defect region is improved in combination with a semantic matcher. A prompt self-adaptive knowledge distillation mechanism is further introduced, so that knowledge migration from the SAM model to the lightweight detection model is realized, and the feature learning ability of complex weld defects is enhanced. According to the method, high-quality defect detection can be realized in the weld defect image without a large number of accurate labels, and particularly, the method has remarkable advantages in the aspect of processing the welding defects with fuzzy boundaries, small sizes or weak contrast, and has wide industrial application value.

The application discloses a long-distance rail recognition and obstacle detection method and system, a long-focus optical system is combined with a super-high-speed industrial camera on a hardware level to build a high-frame-rate and long-distance optical detection system, a region of interest for obstacle detection is demarcated by recognizing and visually segmenting left and right rail lines on a software level, and a target detection-based track obstacle intrusion detection algorithm and other advanced visual tasks are executed, and finally high-frame-rate recognition and detection of long-distance rails and track obstacles are realized. The Schmidt-Cassegrain optical system with a focal length of 2000mm is combined with a super-high-speed industrial camera with a resolution of 1920*1080 and 3000 frames per second to build a long-distance high-frame-rate optical detection platform and erect on a driving position of a train, railway conditions within a range of 6km are detected in real time, and serious accidents caused by a too long braking distance of the train due to high-speed driving can be prevented. Compared with traditional train driver eye observation and laser radar scanning imaging modes, a long-distance optical observation platform and a rear-end real-time detection system can realize a longer detection distance, better detection and recognition robustness and a higher frame rate.

The invention discloses a visual segmentation large model data automatic annotation method combined with image prompt, which comprises the following steps: extracting deep features of a prompt image and a to-be-annotated image by adopting a shared visual coding technology of deep learning, and carrying out cross-image feature matching and similarity calculation. And positioning a high-response area which is consistent with a prompt target in semantics in the to-be-labeled image, and automatically screening out an accurate prompt point set from the high-response area. Furthermore, based on the point set, the visual segmentation large model is guided to complete accurate segmentation and mask generation of the target, so that full-process automation from image prompting to final labeling is realized, and labeling efficiency and consistency are remarkably improved. In this way, the bottleneck problems that in the prior art, an automatic labeling method is insufficient in universality and the application of a segmentation large model must depend on human interaction are solved, and an effective technical approach is provided for large-scale, low-cost and high-quality visual segmentation data production.

This invention relates to the field of bridge structural safety inspection technology, and in particular to a bridge damage assessment method, system, and medium based on a visual segmentation model. The method includes: acquiring prior structural information and post-damage images of the bridge to be inspected; matching the image to be inspected generated based on the post-damage images with the prior structural information and a preset damage semantic knowledge base to generate multimodal prompts; inputting the fused multimodal prompts and the image to be inspected into a visual segmentation model with input parameters frozen, and adaptively iterating to obtain pixel-level segmentation masks for each damage candidate region; superimposing each pixel-level segmentation mask onto the image to be inspected, and inputting it along with the damage analysis prompt template into a visual language model to generate a structured damage assessment report. This invention utilizes the general segmentation capability of the visual segmentation model to achieve rapid and accurate pixel-level segmentation and recognition of sudden bridge damage, and automatically generates a structured damage assessment report.

The invention relates to the technical field of image processing, in particular to an element recognition method and device based on visual segmentation and a storage medium, and the scheme comprises the steps: receiving a positioning parameter set for a to-be-recognized element; performing image acquisition on the to-be-identified element according to the positioning parameter set to obtain at least two to-be-identified images; performing feature recognition on the at least two to-be-recognized images to obtain first coordinate data of each to-be-recognized image in the at least two to-be-recognized images; performing coordinate conversion processing on the first coordinate data of each to-be-identified image to obtain converted second coordinate data; and obtaining target position information of the to-be-identified element according to the positioning parameter set and the second coordinate data. According to the method, through image acquisition, multi-coordinate system conversion and flexible parameter configuration, high-precision, high-adaptability and high-efficiency component identification is realized, the method is especially suitable for a mounting scene of a large component or a special-shaped component, and the reliability and economical efficiency of an automatic production line are remarkably improved.

The application provides a cross-domain small sample wideband signal detection and recognition method based on a visual basic model, and relates to the cross technical field of electromagnetic signal processing and computer vision. The application reduces the interference of background noise in the wideband time-frequency graph on the candidate area by performing double condition screening based on target confidence and aspect ratio features on the preliminary candidate frame, and combining the field of view expansion in the frequency axis direction, and improves the incomplete coverage problem of the slender signal candidate frame. By extracting multiple intermediate layer features and the final layer feature map of the visual basic model for fusion in the channel dimension, the problem that the local texture information in the wideband time-frequency graph is smoothed or covered in the layer-by-layer abstraction process is solved. The parameters of the visual segmentation model and the visual basic model are kept frozen, and only the small sample fine tuning of the front region proposal network is performed, so that the calculation overhead caused by full fine tuning of the visual basic model during cross-domain small sample adaptation is avoided, and the method is suitable for deployment on devices with limited computing resources.

The invention provides a full-autonomous ultrasonic robot real-time liver tumor tracking system, and relates to the technical field of medical images and robots. According to the invention, through deep fusion of a multi-mode force touch sensing closed loop and an image segmentation visual closed loop, full-autonomous ultrasonic tracking of liver tumors is realized. Mechanical sensing ensures that the probe is always in stable contact with a patient in a proper posture and force, and visual segmentation feedback ensures that the probe is continuously aligned with and focuses on a tumor target. Under the synergistic effect of the two paths of feedback, the robot can automatically complete the whole process from probe placement, stable attachment, target recognition to real-time tracking, the uncertainty of manual operation is greatly relieved, and the safe, efficient and intelligent liver tumor real-time ultrasonic monitoring robot system is achieved.

The present invention discloses a method for visual segmentation and recognition of Agaricus bisporus, including collecting images of Agaricus bisporus and preprocessing the images of Agaricus bisporus to obtain a mushroom foreground image; performing a marking process on the mushroom foreground image to produce a watershed marking image; using the watershed marking image as a seed growth point to divide similar regions in the watershed marking image to complete the segmentation of Agaricus bisporus; extracting the contours of the divided regions and obtaining the center and diameter of a single Agaricus bisporus monomer through the minimum circumscribed rectangle algorithm to complete the recognition of Agaricus bisporus; the present invention accurately filters out complex backgrounds by designing a tunable filter, and combines the watershed transformation algorithm and an edge detection template to achieve precise segmentation and recognition of Agaricus bisporus.

The invention discloses a structure multi-target deformation monitoring method based on visual segmentation and deblurring enhancement, and relates to the technical field of engineering structure health monitoring. The method comprises the following steps: acquiring continuous time sequence multi-target images of different depth-of-field planes of a to-be-monitored structural plane; inputting an interactive prompt point into a pre-training segmentation model in the first frame to generate a multi-target mask, and removing a noise region based on connected domain analysis; cutting by adopting a self-adaptive multi-scale cutting strategy based on boundary judgment to obtain sub-graphs; performing image enhancement on the sub-image by adopting a pre-trained deblurring neural network; extracting a central point and an external rectangle coordinate of a target mask area in the sub-graph and uniformly mapping the central point and the external rectangle coordinate back to a full-graph coordinate system; and dynamically updating a cutting window of a subsequent frame based on the central point and boundary information of the first frame, and converting pixel-level displacement into physical unit or three-dimensional coordinate change by comparing the central point positions of the first frame and the subsequent frame and combining camera calibration parameters, thereby realizing high-precision continuous monitoring.