1160 results about "View camera" patented technology

The embodiment of the invention provides a three-dimensional model adaptive generation method based on video and point cloud data, scene data is collected through a multi-view camera array and a depth scanning array, and key features are extracted and screened by using a hierarchical feature extraction network and an attention mechanism. And organizing local data blocks by adopting a hierarchical index tree based on a union-check set, and realizing self-adaptive parameter optimization by combining integrity and accuracy scores. Meanwhile, efficient reconstruction and material mapping of a local three-dimensional grid are realized through a parallel operation channel and a dynamic calculation resource scheduling mechanism. According to the method, the precision and efficiency problems of a traditional three-dimensional reconstruction scheme in processing a large-scale scene are effectively solved, and the model generation quality is improved.

An apparatus includes a base having a platform configured for placement of one or more items on a surface of the platform, a frame coupled to the base, and a plurality of camera units coupled to the frame. Each camera unit is configured to capture images of at least a portion of the platform and the plurality of camera units includes a plurality of side-view camera units arranged above the platform along a horizontal plane, wherein each camera unit of the side-view camera units comprises two vertically stacked cameras and is configured to capture perspective images of at least a portion of the platform. An overhead-view camera unit is positioned over the platform, wherein the overhead-view camera unit comprises two cameras and is configured to capture top view images of the platform.

The invention provides a rehabilitation training action evaluation method and device based on multi-view vision. According to the method, a multi-view camera is adopted to synchronously collect a rehabilitation training image sequence, two-dimensional coordinates of key points of a human body are extracted by improving an HRNet deep learning model, three-dimensional reconstruction is performed in combination with a Gaussian process, motion feature data are extracted, and rehabilitation training quality is evaluated. Accurate motion capture without wearing mark points by the patient is realized, the training constraint feeling of the patient is effectively reduced, the rehabilitation evaluation accuracy is improved, and a personalized rehabilitation scheme can be generated.

The invention discloses a mechanical arm positioning and grabbing method based on machine vision, and relates to the technical field of machine vision and mechanical arm control, the method comprises the following steps: synchronously acquiring RGB-D images of a target scene through a multi-view camera array, and generating three-dimensional point cloud data through data fusion; an improved LSD algorithm and a PnP algorithm are adopted to calculate the initial pose of the target object, illumination distortion is eliminated in combination with the generative adversarial network, and three-dimensional coordinates are output; a mechanical arm motion error transfer model is constructed based on Monte Carlo simulation, and a candidate grabbing scheme set is generated through reinforcement learning; and an optimal grabbing scheme is screened through a preset priority evaluation rule, and a mechanical arm joint movement track and a control instruction set are generated. Through multi-modal data fusion and a nonlinear optimization algorithm, the technical problems of large target positioning deviation and sensitive illumination interference in a complex environment are solved, and the grabbing precision and robustness of the mechanical arm are improved.

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention relates to the technical field of automatic workpiece grabbing and visual servo control, and discloses a workpiece grabbing method and system based on visual feedback, and the method comprises the steps: obtaining a real-time image through a front-view camera, a side-view camera and a top-view camera, and extracting surface features through a feature fusion network; inputting the features into a pose estimation model based on a particle filtering framework, and iteratively updating the pose in combination with a multi-dimensional observation likelihood function to generate prediction parameters; constructing a multi-target path planning model based on the parameters, and optimizing the path by adopting a dynamic planning algorithm with the shortest path and the minimum joint movement as targets; a hierarchical control model is established, a strategy layer performs global planning, an adjustment layer corrects a local path, and an execution layer realizes trajectory tracking through visual servo control and outputs a control instruction to complete grabbing. Through multi-view perception, robust pose estimation, global optimization path planning and hierarchical control, the precision, efficiency and stability of workpiece grabbing in a complex environment are improved.

The invention relates to the technical field of tunnel engineering construction, and discloses an intelligent material transportation and safety monitoring system and method for shield construction, and the system comprises a visual perception unit, a sensor network module, an AI analysis center module, a safety decision module and a human-computer interaction interface. According to the invention, data acquisition is carried out through the visual perception unit and the sensor network module, multi-target detection operation is carried out on image frames through the AI analysis center module after target identification and track prediction, target types, space coordinates, contour boundaries and confidence coefficients are identified and extracted, and safety judgment and early warning output are carried out. According to the invention, by integrating the multi-view camera equipment and the UWB, GNSS and other sensors and adopting a deep learning target detection algorithm, high-precision identification and continuous tracking can be carried out on construction site personnel, equipment, segments and other key objects, and accurate input is provided for subsequent risk analysis.

The invention relates to the technical field of unmanned aerial vehicle autonomous control, and discloses an unmanned aerial vehicle obstacle avoidance control method and system based on computer vision, and the method comprises the steps: collecting the multi-view image data of a flight environment in real time through a multi-view camera, and carrying out the preprocessing; using an improved YOLOv7 network to identify obstacles in the preprocessed image, and extracting position, size and motion information of the obstacles; generating a three-dimensional environment map and a plurality of candidate obstacle avoidance paths in combination with the flight parameters of the unmanned aerial vehicle and the extracted obstacle information; an optimal path is screened based on a dynamic safety evaluation model, and the attitude and power output of the unmanned aerial vehicle are adjusted in real time to complete obstacle avoidance; an obstacle avoidance effect is verified by using an optical flow method and depth information, and path planning is dynamically corrected. According to the method, the dynamic safety evaluation model is introduced, multi-dimensional factors are comprehensively considered, the safety of each path is dynamically determined, and it is ensured that the flight path of the unmanned aerial vehicle can be timely and accurately modified and optimized in a dynamic complex environment.

The invention relates to the technical field of automatic driving, and particularly discloses an automatic driving interpretation text determination method based on a large visual language model, and the method comprises the steps: obtaining a multi-view camera image, a BEV map image and a text query template; video features in the multi-view camera image are extracted, and image features in the BEV map image are extracted; aligning the image features with the video features based on a modal encoder; encoding the text query template, generating a text mark, mapping the aligned image features and video features to a text embedding space based on a shared projector, generating a visual mark, and inputting the visual mark and the text mark into an LLM backbone model; comprehensive three-dimensional dynamic / static situation perception and visual representation unification of a local scene and a global map are realized, the 3D LVLM architecture has excellent three-dimensional space understanding and dynamic time reasoning capabilities, and the accuracy and safety of end-to-end automatic driving are effectively improved.

The invention discloses a public transport passenger flow monitoring method and system based on human body detection, and the method comprises the steps: synchronously collecting skeleton key points, heat distribution and contour features of passengers through a vehicle-mounted multi-view camera and an infrared sensor, and generating a passenger space-time feature matrix; outputting a passenger movement track set based on the passenger space-time characteristic matrix; generating a dynamic congestion degree index according to the staying duration and the spatial distribution density of the passengers in the passenger movement track set; calculating an abnormal behavior probability based on the dynamic congestion degree index, and outputting an abnormal event early warning signal; and fusing the dynamic congestion degree index, the abnormal event early warning signal and the real-time passenger flow data, and generating a bus passenger flow monitoring result through space-time correlation analysis. According to the embodiment of the invention, high-precision, low-delay and intelligent passenger flow state monitoring and abnormity early warning can be realized.

The invention discloses a three-dimensional attitude high-precision acquisition and reconstruction system for exercise training, which relates to the technical field of three-dimensional attitude acquisition and reconstruction and comprises a camera calibration module, an image acquisition module, a key point extraction module, a three-dimensional reconstruction module, an attitude optimization module, a data fusion module and a feedback generation module. The camera calibration module is used for performing internal and external parameter calibration on a multi-view camera assembly by adopting a self-adaptive joint calibration method to obtain a projection matrix parameter and a transformation matrix of each camera; the image acquisition module is used for synchronously acquiring multi-view video images of the athlete in the training process based on the calibration parameters, and performing timestamp alignment processing to obtain an original image sequence; and the key point extraction module is used for detecting and identifying a human body image by adopting a deep learning model, extracting two-dimensional coordinates of key human body joint points of each frame from an original image sequence, and obtaining a two-dimensional key point set under each view angle.

The invention discloses a law and regulation simulation test method, device and equipment for an intelligent driving algorithm and a storage medium, and relates to the technical field of intelligent driving, and the method comprises the steps: carrying out the high-precision three-dimensional reconstruction of a law and regulation test site according to collected data, and obtaining a three-dimensional reconstruction scene, and the collected data comprises a laser radar point cloud and a multi-view camera image; obtaining simulation working condition test data based on the three-dimensional reconstruction scene; training a network model according to the simulation working condition test data and the real vehicle working condition test data to obtain a vehicle dynamics network model; and based on an automatic test program, constructing a closed-loop simulation link according to the three-dimensional reconstruction scene and the vehicle dynamics network model, and carrying out regulation simulation test on an intelligent driving algorithm. According to the invention, the efficiency and accuracy of intelligent driving regulation testing can be improved.

The invention discloses a target detection method and device based on an aerial view, equipment, a medium and a product. The method comprises the following steps: extracting multi-scale features for an input image of a multi-view-angle surround-view camera; converting the point cloud data of the laser radar into a sparse voxel structure and generating BEV features for voxel features; fusing the multi-scale features and the BEV features to obtain fused BEV features; road topology and semantic information are extracted according to the fused BEV features, and a map is generated; and determining a three-dimensional bounding box and a motion state of the traffic participant according to the fused BEV features and a prediction result of the position of the traffic participant in the map. According to the technical scheme, multi-scale geometric features, BEV features and semantic information are fused, time sequence features of dynamic target detection and static map segmentation are synergetic, the reliability of target detection based on aerial view is improved, and the method is suitable for multi-task BEV perception of three-dimensional object detection, high-precision map segmentation and motion prediction.

The invention relates to a three-dimensional occupied grid network sensing method, which comprises the following steps of: acquiring a multi-view camera image shot by automatic driving of a vehicle, inputting the multi-view camera image into an image processing backbone network, and extracting two-dimensional image features; performing view angle conversion on the two-dimensional image features to obtain three-dimensional body features; constructing matching cost body features according to the geometric correspondence of the sampling points at different moments; a convolutional neural network is adopted to extract weights containing depth correction information from the matching cost body features, the weights act on the three-dimensional body features at the current moment, and the three-dimensional body features after depth information correction are obtained; and inputting the three-dimensional body features into an occupied grid decoder, projecting the three-dimensional body features to a discrete semantic space, and generating a three-dimensional semantic occupied grid. Compared with the prior art, by constructing the matching cost body, more accurate depth information and geometric understanding can be obtained, the calculation overhead under a long time sequence is reduced, and accurate and efficient modeling of the automatic driving environment can be realized.

The invention relates to the technical field of automobile door opening early warning, and provides an intelligent driving domain control fused door opening early warning method and device and an automobile. The method comprises the following steps: when a micro-control unit is powered on and awakened to start working, receiving a first target alarm signal sent by a radar, and forwarding the first target alarm signal to a domain controller; after the on-chip system connected with the multi-view camera is powered on and started, radar target detection data are obtained from the micro-control unit, an image collected by the multi-view camera is recognized, image target detection data are obtained, a second target alarm signal is determined according to the radar target detection data and the image target detection data, and the second target alarm signal is sent to the micro-control unit. A second target alarm signal is periodically sent to the micro-control unit; according to the invention, target fusion is carried out through the radar and the camera, so that the tracking alarm of the door opening early warning function on special-shaped targets and general targets is expanded, and the usability of the door opening early warning function is improved.

An interactive gaming system comprising a multiplicity of game access points used by guests to engage with an on-going destination game. Game access points comprise traditional destination activities as well as additional game parts. Gaming guests carry one or more electronic means and are tracked throughout the destination locations and activities. At least one electronic means comprises a visual display for receiving requested directions or a system summons to a game access point. The electronic means comprises a direct view lens system such that the guest looks directly through the electronic means to a destination map or destination scene, where directions are overlaid onto the direct view and registered to the map or scene. The mobile device further comprises a front view camera for capturing images of the guest's direct view of a destination game part, where the image is used as actionable game information.

The invention discloses a calibration method, system and device for a vehicle around-view camera and a computer medium, and relates to the technical field of image processing, and the method comprises the steps: collecting a calibration plate image obtained after a calibration plate is shot by the around-view camera on a vehicle; projecting the calibration plate image to a world coordinate system to obtain a distorted image; selecting an initial angular point in the distorted image according to the calibration plate; projecting the initial angular point to a fisheye image coordinate system of a look-around camera to obtain a target angular point; according to the target angular point, generating an initial external parameter of the look-around camera; in the calibration plate image, extracting overlapped corner point parameters in the overlapped shooting area; and based on the initial external parameter, generating a target external parameter which enables the error between the overlapping angular point parameters to be minimum, and taking the target external parameter as the external parameter of the surround-view camera. According to the method, the extraction precision and robustness of the angular points are improved, the continuity and smoothness of the images shot by the surround view camera are improved, splicing gaps and dislocation phenomena are reduced, and the calibration result of the vehicle surround view camera is more accurate.

The invention belongs to the field of computer vision, and particularly relates to a method, a device and a system for monitoring suction-scrabbling falling of a cleaning robot, and the method comprises the following steps: 1) a rear-view camera arranged on the cleaning robot collects suction-scrabbling images, and carries out data labeling; 2) building a deep learning labeling and training environment in a server or a workstation, selecting a YOLOv8 target detection network model, sending labeled suction and scrabbling image data into the model for training, and performing format conversion and quantization on the model after the training is completed; 3) inputting an image acquired in real time into the trained target detection model, and judging whether the suction rake falls off or not; (4) if the suction scrabbling device falls off, the upper computer sends falling abnormal information to a robot controller of the cleaning robot, and the robot controller controls a hub motor to brake emergently; and meanwhile, the upper computer transmits the abnormal information of the suction-scrabbling falling and the position coordinate information of the robot in the global map to the operation and maintenance personnel through the cloud platform and then transmits the abnormal result and the position information of the suction-scrabbling falling to the operation and maintenance personnel.

The invention provides a camera three-dimensional target detection method and system based on multi-modal distillation, and relates to the technical field of target detection, and the method comprises the steps: determining a trained multi-modal teacher model employing a point cloud and a multi-view camera image as the input, performing cross-modal comparison distillation on the constructed student model taking the multi-view camera image as the input by using the point cloud features in the teacher model, so that the student model has the space geometry reasoning ability to construct the false point cloud features, and performing multi-modal adaptive smooth distillation to obtain the multi-view camera image. And aligning student model features obtained based on heterogeneous fusion of the false point cloud features and the image features and teacher model features obtained based on channel fusion of the point cloud features and the image features, when the regression output of the teacher model is superior to the regression output of the student model, selecting to distill the regression frame knowledge into the student model, and through the above training process, obtaining the teacher model. The structure consistency of the student model and the teacher model can be ensured, and the three-dimensional target detection accuracy of the student model is improved.

The invention provides a traffic track deformation detection method and system based on multi-modal three-dimensional point cloud fusion, and relates to the technical field of data processing, and the method comprises the steps: carrying out the track data collection through a multi-view camera and a laser radar which are integrated on a track detection vehicle; configuring the dynamic credibility of the time sequence multi-modal data set, and performing time sequence data three-dimensional point cloud fusion on the time sequence multi-modal data set; establishing a driving data set; and performing target attention detection on the three-dimensional point cloud fusion result and the driving data set by using a multi-modal three-dimensional target detection network, and outputting a three-dimensional bounding box and classification confidence of the track deformation region according to a target attention detection result. The technical problem that the accuracy of track deformation detection is low due to the fact that the precision of multi-source data fusion is insufficient is solved, the precision of data fusion is improved through multi-modal data fusion, the fused data is detected through the multi-modal three-dimensional target detection network, and the timeliness and accuracy of track detection are improved.

The invention provides a scene semantic occupancy prediction method based on probabilistic three-dimensional Gaussian, and the method comprises the steps: processing a multi-view camera image through employing a two-dimensional backbone network, generating multi-view and multi-scale image features, predicting the three-dimensional occupancy distribution aligned with pixels through the image features, and initializing the position attribute of the three-dimensional Gaussian according to the distribution; the method comprises the following steps: taking three-dimensional gauss as point cloud data, generating sparse voxel data through voxelization, and processing the sparse voxel data by using three-dimensional sparse convolution to complete feature interaction between the three-dimensional gauss; by using a cross attention mechanism, updating three-dimensional Gaussian features by image features; performing layer-by-layer iterative optimization on each attribute in the three-dimensional Gaussian features by using a Gaussian attribute fine tuning model; and aggregating geometric attributes and semantic attributes of the three-dimensional Gaussian by using a probability Gaussian superposition method, and generating a three-dimensional semantic occupation prediction result of the scene. According to the invention, the problem of efficient adaptive three-dimensional semantic occupancy prediction of an automatic driving scene can be solved.

The invention discloses a lightweight three-dimensional human body posture estimation method for an uncalibrated multi-view camera, and the method comprises the steps: firstly collecting a three-dimensional human body posture estimation data set of any view angle number, and defining a two-dimensional posture sequence from # imgabs0 dynamic view angles and a corresponding three-dimensional posture sequence; secondly, extracting features of the multi-view two-dimensional attitude sequence by using a bidirectional Mama model to obtain a prediction result, and pre-training and finely adjusting the bidirectional Mama model; and finally, training the bidirectional Mama model end to end by using a multi-view-angle data set shot by the uncalibrated movable camera, outputting a three-dimensional posture of each view angle, and aligning the postures of different view angles to one of the view angles by means of protoustes to complete human body posture estimation. According to the method, multi-view attitude estimation is adopted, so that the human body attitude of a person can still be estimated with high precision in an environment with more shelters, and a camera calibration process is avoided.

The invention discloses a white balance intelligent adjustment and optimization system and method for multi-view camera equipment, and the system comprises a multi-view image collection unit which is used for capturing images of an environment where the multi-view camera equipment is located from different angles, and forming a multi-angle view; the ambient light detection module is used for evaluating the illumination condition of the environment where the multi-view camera is located and providing measurement data of illumination intensity, color temperature and direction; the ambient light color temperature analysis algorithm is used for calculating the color temperature value of the current environment; and the intelligent white balance adjusting unit is used for automatically adjusting the white balance setting of the multi-view camera according to a result calculated by the ambient light color temperature analysis algorithm. The light condition and the shooting environment of the multi-view camera equipment are detected in real time through the environment light detection module, and the white balance parameters of the multi-view camera are intelligently adjusted according to the factors, so that the white balance adjustment precision and efficiency of the multi-view camera equipment can be greatly improved, and more accurate and real images are obtained.

The invention relates to a tunnel three-dimensional holographic splicing method and device for panoramic array camera networking, and the method comprises the steps: enabling a reference transmission camera array and a three-dimensional laser scanner on an inspection vehicle to move to different inspection positions along with the inspection vehicle for adjustment, achieving the transmission of peripheral control information at two ends of a tunnel into a measurement region, and constructing a dynamic control field; the limitation on measurement conditions is reduced; high-precision positioning and attitude determination are carried out based on fixed connection constraints of a look-around camera array and a three-dimensional laser scanner and a constructed dynamic control field, so that a measurement platform is unified to a global coordinate system, and the inherent defect of large accumulative error of a traditional three-dimensional modeling method is effectively avoided; and then the high-resolution look-around image is mapped to the three-dimensional point cloud data under the global coordinate system, so that high-resolution and high-precision three-dimensional holographic splicing of the tunnel ring surface under the global coordinate system is realized, global and local high-resolution and high-precision are achieved, and the acquisition precision of the tunnel three-dimensional hologram is greatly improved.

The invention relates to the technical field of sports artificial intelligence and data management, in particular to a physical exercise AI standing long jump evaluation data management system which comprises a dynamic environment calibration module, a multi-mode identity recognition module, a three-dimensional posture analysis module and the like. The dynamic environment calibration module uses a multispectral sensor array and a deep learning algorithm to establish a dynamic coordinate system without fixing a marker; the multi-mode identity recognition module fuses gait and face features to realize non-contact identity authentication; the three-dimensional attitude analysis module performs high-precision modeling and analysis on the long jump action through a multi-view camera and an advanced algorithm; the intelligent violation determination engine combines rules and an AI model, automatically identifies violation behaviors and can dynamically adjust determination criteria. The method gets rid of the limitation of a traditional evaluation mode, realizes high-precision and intelligent evaluation, effectively solves the problems of poor environmental adaptability, inaccurate identity authentication, unguaranteed data security and the like, and is scientific and reliable in evaluation result and safe and efficient in data management.

Disclosed herein is a point of view camera system. The point of view camera system includes a camera mount configured to attach to a headwear, the camera mount including a base configured to secure the camera mount to the headwear, a first camera including a first image sensor and a first camera lens, a second camera including a second image sensor and a second camera lens, and a camera housing configured to attach to the camera mount. The camera housing includes a first opening configured to receive and secure the first camera and a second opening configured to receive and secure the second camera.

The invention provides an intelligent heavy truck end-to-end driving method based on focus attention and probabilistic game, and belongs to the technical field of intelligent driving. The method comprises the following steps: firstly, acquiring multi-source data such as a laser radar, a look-around camera, an IMU (Inertial Measurement Unit) and a load signal, and generating an anti-jitter space-time BEV feature through cross-dimensional position coding based on motion compensation, near-field non-uniform sampling, focus attention and time sequence fusion; then, vectorization decoding is carried out on the features to obtain obstacle vehicle movement and map element vector information. And finally, on the basis of the expert track prior space, probability game interaction is carried out through multi-round iterative prediction and planning, a self-vehicle track conforming to heavy truck variable load dynamics constraints is generated, and the self-vehicle track is output after safety verification. According to the method, the problems of heavy truck high-position sensor vibration distortion, hinge structure blind area and trajectory planning under the large-inertia variable-load working condition are effectively solved, and the driving safety and robustness are remarkably improved.

A vehicular driver monitoring system includes a vehicular interior rearview mirror assembly having a mirror head that accommodates a mirror reflective element. A video display is disposed behind the mirror reflective element and operable to display video images captured by a rearward viewing camera of the vehicle. A driver monitoring camera and a near infrared light emitter are accommodated by and move in tandem with the mirror head. The near infrared light emitter is accommodated within the mirror head so that, with the mirror head adjusted relative to the mounting base to set the rearward view of the driver of the vehicle, a beam of near infrared light emitted by the near infrared light emitter is directed toward a driver's region of the vehicle. The driver monitoring camera is disposed adjacent to the video display screen so as to not view through the video display screen.

The invention belongs to the technical field of laser radar-camera panoramic segmentation, and particularly relates to a multi-mode panoramic segmentation method for multi-view space-time alignment and implicit feature interaction, and the method is executed by a multi-mode panoramic segmentation network, and comprises the steps: S1, obtaining laser radar point cloud data and multi-view camera image data in the same scene; s2, performing double-branch feature coding on the laser radar point cloud data; performing multi-scale image feature extraction on the camera image data; s3, generating false point cloud features with geometric perception capability; s4, generating semantic pixel features; s5, performing implicit fusion on the pseudo point cloud features generated in the S3 and the semantic pixel features generated in the S4 to obtain cross-modal fusion features; and S6, based on the cross-modal fusion features obtained in the S5, generating a unified panoramic segmentation result containing semantic tags and instance IDs. The method can effectively improve the robustness and precision of multi-mode panoramic segmentation in a complex urban environment.

The invention discloses a microwave nondestructive testing device and method for aging of a polyethylene pipe and appendages. The microwave nondestructive testing device comprises a microwave antenna, a multi-view camera, a control module and a display module. The microwave antenna transmits and receives signals to obtain internal structure information, and the multi-view camera captures high-resolution images for auxiliary analysis. The control module realizes multi-degree-of-freedom movement of the device, performs preprocessing, feature extraction and intelligent analysis on acquired signals, identifies aging features such as cracks, bubbles and layering through a deep learning algorithm, and remotely transmits data. And the display module displays the detection result and the aging evaluation report in real time. The device combines microwave imaging and camera images, realizes accurate defect identification, real-time monitoring and data comparison, supports automatic obstacle avoidance and pipeline safety evaluation, improves the detection precision and efficiency, and is suitable for aging detection of various types of pipelines and accessories.