105 results about "Visual segmentation" patented technology

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 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 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 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.

The invention provides a context compression method and system based on a visual modality and a medium, and the method comprises the steps: obtaining long text context data, and rendering the long text context data into a corresponding document image; performing context optical coding on the document image, and performing segmentation processing on the document image by adopting a visual segmentation model to obtain a plurality of initial visual tokens; performing down-sampling compression on the initial visual token based on a convolution compression algorithm to obtain a compressed token; capturing a long-distance dependency relationship among different compression tokens based on a global attention mechanism, extracting high-level semantic knowledge, and outputting a final visual token sequence; executing context optical decoding on the final visual token sequence, and fusing context optical decoding data with a text prompt input by a user to generate a target text; through visual modal compression, computing resources and memory occupation required for processing a long text are reduced, and relatively high text decoding precision can still be maintained while a high compression ratio is maintained.

The invention relates to the field of image processing, and provides a visual segmentation counting method and system suitable for disorderly stacked parts, which can avoid the influence caused by a complex construction environment by carrying out dynamic Gamma correction and overlapping region enhancement processing on part images of multiple visual angles, highlight and enhance the characteristics of the overlapping region, and improve the accuracy of the visual segmentation counting of the disorderly stacked parts. Specific detail information of a shielding part and a small target is easier to recognize, recognition accuracy and robustness are improved, basic semantic segmentation is performed through a lightweight CNN model, recognition precision is guaranteed, meanwhile, calculation amount is greatly reduced, recognition efficiency is improved, and through dynamic threshold segmentation, seed point production and region growth, recognition efficiency is improved. According to the visual segmentation counting method for the disordered and stacked parts, the semantic and geometric features of the image are combined, the shielded and small-size parts are segmented more accurately, the omission ratio is effectively reduced, higher adaptability and detection precision are shown in a complex scene, and the accuracy and efficiency of the visual segmentation counting method for the disordered and stacked parts are improved.

This invention relates to the field of scene text segmentation, specifically a scene text segmentation method based on an improved SAM visual segmentation large model. Based on the SAM visual large model, this invention extracts text content perception features through an image content perception module and text edge perception features through a text edge perception module. Furthermore, the text feature fusion module extracts and calculates text edge perception feature maps, which are then added to the vectors requiring attention calculation before each self-attention calculation in the SAM encoder. This improves the accuracy of SAM in text segmentation and shortens the model training time while maintaining generalization.

The application discloses a kind of filtering and remaining area spray trajectory replanning method and system across station has been sprayed area, belong to industrial robot vision and automatic spraying technical field.This system is composed of mobile chassis, six-axis robot arm, terminal RGB-D camera, spray gun, station visual label and supporting function module;Robot generates standard spraying trajectory by visual segmentation, point cloud processing in first station and records sprayed area data, after moving across station, relies on visual label to complete multi-frame fusion relocation, calculates inter-station coordinate transformation matrix, maps historical sprayed area to current coordinate system and eliminates from real-time point cloud, re-plans serpentine spraying trajectory for remaining to be sprayed area, and accurately controls spray gun on-off in combination with trajectory segmentation logic.The application solves the problem that traditional mobile spraying robot is prone to repeated spraying, uneven paint film and repeated teaching when working across station, realizes multi-station continuous automatic spraying, and the trajectory runs smoothly with high spraying consistency.

The invention discloses an audio-visual segmentation method based on cross-modal cognitive consensus alignment, belonging to the field of multi-modal image segmentation. A cross-modal cognitive consensus module and a cognitive consensus-guided attention module are proposed. The cross-modal cognitive consensus module calculates the classification confidence of audio and vision respectively, and measures the mutual similarity of audio-visual semantic labels. Then, the mutual similarity is weighted by the classification confidence to obtain the semantic-level cross-modal cognitive consensus score and select the semantically aligned labels. Subsequently, the gradient of the semantically aligned labels is backpropagated to the visual encoder to obtain class activation information. Through the cognitive consensus-guided attention module, visual targets with high semantic consistency are highlighted to guide the subsequent segmentation process. On the one hand, the method of the invention achieves the current state-of-the-art performance on the audio-visual segmentation dataset. On the other hand, the method of the invention can accurately and effectively segment the vocal targets in the video and output pixel-level masks.

The invention discloses a 3D printing concrete interface pore structure visual segmentation method based on point cloud. The method comprises the following steps: 1, collecting point cloud data; 2, preprocessing the collected point cloud data; 3, dividing the point cloud data set into a training set, a verification set and a test set; 4, performing point cloud data sampling on the training set of the point cloud data set in the step 3 to obtain a local relation; 5, building a feature detail extraction network in the 3D printing concrete interface pore structure visual segmentation network model PointESamba by using a multi-layer sensor, and carrying out detail extraction on sampled point cloud data features of the pore structure; step 6, optimizing the disorder of the point cloud through an adaptive sorting algorithm; and step 7, obtaining a most adaptive sequence by using the sorting algorithm in the step 6, obtaining global features by using a Mama module, further segmenting and identifying the features extracted in the step 5, and completely constructing PointESamba. According to the invention, the segmentation precision and capability of the point cloud on the pore structure are enhanced.

The invention provides a memory parking cross-layer identification method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent driving. In the method, a vehicle cross-layer state is determined through pitch angle features and a visual semantic segmentation result; the pitch angle features comprise a pitch angle mean value, a pitch angle variance and a difference value between the latest frame of pitch angle data and the oldest frame of pitch angle data in the sliding window, cross-layer identification failure caused by value abnormity due to direct use of the pitch angle data can be avoided, the correctness of the cross-layer state of the vehicle is ensured in combination with a visual segmentation result, and in addition, the accuracy of the cross-layer state of the vehicle is improved. Through a set of state switching logic, identification of the cross-floor state of the multi-floor underground parking lot (namely, the height of the current floor where the vehicle is located is determined according to switching information) is achieved, the accuracy is good, and the reliability is high.

This invention belongs to the field of shipbuilding and surface treatment technology, specifically relating to a visual inspection method and device for a laser rust removal climbing robot for large flat surfaces in ship sections. It is applied to secondary rust removal operations on the flat surfaces of ship sections during the manufacturing process, using a laser cleaning climbing robot. Particularly, it relates to a technical solution that uses visual rust detection results to identify rust conditions in real time and automatically set laser cleaning process parameters by matching them to a process parameter library. Furthermore, this invention also relates to a method for accurately determining the rust range using visual segmentation technology to plan the laser cleaning operation area, and a closed-loop control technology for real-time inspection of the rust removal effect using a vision system after laser cleaning. It also relates to lens protection measures for the camera module in the laser cleaning operation environment. This invention can be widely applied to automated laser cleaning operations of large flat structures in shipbuilding, marine engineering equipment, and steel structure surface treatment fields, improving the overall level of automation.