392 results about "Hough transforms" patented technology

The invention relates to the technical field of image processing, and discloses an industrial article appearance detection method and system based on machine vision. The method comprises the steps that a workpiece image is collected, and a standardized image is obtained through Gaussian filtering, histogram equalization and Otsu algorithm preprocessing; feature vectors are extracted through edge detection, Hough transform and a gray-level co-occurrence matrix; training a defect identification model by adopting a CNN of an attention mechanism; inputting an image for real-time detection; fusing the detection data and the environmental parameters by using a D-S theory; and establishing a database based on a distributed architecture to generate a quality report. According to the method, the detection accuracy is improved through multi-source information fusion, and efficient management and analysis of mass detection data are realized by adopting a distributed architecture.

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention discloses a full-automatic cable vibration high-precision measurement method based on a machine vision technology, and relates to the technical field of structural vibration measurement. The method comprises the following steps: installing and calibrating equipment, and ensuring that a cable plane coincides with an imaging plane; high-frame-rate video acquisition is carried out, and comparison verification is carried out in combination with an accelerometer; screening effective pixels based on the pixel intensity time sequence variance, and constructing a pixel intensity space-time matrix by using the time sequence of the pixels; sVD decomposition is adopted to extract an accumulated energy ratio gt; 90% of the main modes are reconstructed, and real signals are revealed; a plurality of inhaul cables are automatically positioned and coded through gradient features and Hough transform; sub-pixel-level vertical displacement is calculated by applying an optical flow method, and median filtering is combined to resist noise; fourier transform is carried out to obtain a spectrogram, and a fundamental frequency is identified; and calculating cable force based on a string pulling theorem. According to the method, remote, full-field and multi-target synchronous monitoring is realized, the displacement measurement precision reaches a sub-pixel level, and a high-precision and full-automatic solution is provided for health monitoring of structures such as bridges.

A method for measuring the size of a slide block of a guide rail with a complex shape based on intelligent vision belongs to the field of machine vision, and comprises the following steps: DXF file data acquisition: reading by using Python; image acquisition and preprocessing: acquiring image data in real time by using an industrial camera, and performing image preprocessing; hole detection and measurement: adopting a Hough transform algorithm to realize sliding block hole detection, introducing a sub-pixel counting method to improve the algorithm, and realizing aperture high-precision measurement; edge line detection and measurement: extracting edge and line segment features of the sliding block image by adopting Canny edge detection and LSD line segment detection technologies, automatically removing invalid line segments, determining an optimal fitting line segment, and realizing accurate detection and measurement of a target edge line; and detection and measurement result visualization: displaying a sliding block size measurement result in an image, comparing the sliding block size measurement result with a standard parameter of a DXF file, judging the qualification of the sliding block, constructing a visual interface by adopting Pyside2 to record the measurement result, and supporting a plurality of input source detection functions.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

Techniques for estimating a ground profile, including a ground height (e.g., elevation) and ground roll, for an environment associated with a vehicle are described herein. Sensor data associated with a vehicle may be used to determine sensor data points associated with a path of the vehicle. Based on an upper bound slope and lower bound slope, such as a maximum and / or minimum rate of ground roll change, a region (e.g., search window) may be determined. Based on the region, a subset of sensor data points included in the region may be determined. The subset of sensor data points may be used to determine a representative height and representative roll. In some instances, the representative height and representative roll may be determined using a Hough transform and the subset of sensor data points. A ground profile may be determined based on the representative height and representative roll.

The invention provides an automatic identification method and system for a linear structural plane in a complex rock mass outcrop, and belongs to the field of rock mass engineering, and the method comprises the steps: obtaining a DeepLabV3 + model of a rock mass outcrop image, carrying out the semantic segmentation of an input image, outputting a binary mask image of the trace pixel of the structural plane, and carrying out the topology reconstruction; establishing a mapping relation between the image coordinate system and the three-dimensional point cloud; calculating a space included angle between the direction vector of the trace and the average occurrence of the structural plane groups, classifying the three-dimensional point cloud data according to the structural plane groups according to an angle threshold value, and performing three-dimensional line segment extraction by adopting an improved iterative Hough transform algorithm; and realizing global multi-segment connection and trace length calculation of discrete segments based on a greedy algorithm. The method breaks through the limitation of a topographic region when the rock mass structural plane data is obtained through manual measurement, and improves the precision and efficiency of structural plane extraction in the complex rock mass.

The invention discloses a photovoltaic cleaning control method fusing RTK positioning and visual compensation, and belongs to the technical field of photovoltaic module cleaning. According to the invention, through the RTK track points acquired offline, the least square method is combined with the RANSAC algorithm to carry out segmented straight line fitting, and the original points are projected to the corresponding straight lines to realize collinear correction, so that the measurement error is eliminated, and a continuous and collimated navigation path can be generated; edge lines (photovoltaic module grid lines) with geometrical characteristics in the environment are detected in real time through a visual sensor, the line segment direction is extracted through Hough transform, the line segment direction is compared with the target course calculated through RTK, course deviation is estimated, dynamic correction is conducted through a control strategy, and it can be ensured that the photovoltaic cleaning robot operates along the expected path with high precision; and on the basis of a lightweight YOLOv7 model, the photovoltaic panel is subjected to pollution category division and mapped to optimal water pressure and brush speed parameters, cleaning parameters are dynamically modified, and then cleaning strategy control is completed.

The invention discloses a PDF (Portable Document Format) drawing recognition and information structured extraction method. The method comprises the following steps: generating a high-resolution bitmap through image preprocessing; positioning and classifying a text region, a table region and a symbol region in the drawing based on a target detection model of transfer learning; hough transform is combined with SIFT feature matching to identify engineering symbols, and sub-pixel positioning is realized through an RANSAC algorithm; after the oblique text is corrected through affine transformation, the content is extracted through OCR; reconstructing a table structure based on OPTICS clustering and projection analysis; constructing an RDF knowledge graph according to a coordinate association rule; and using U-Net difference to detect and position an omission area and complementing the omission area. According to the method, deep learning and image processing technologies are fused, the problems of low rotating text recognition rate, table structure loss and semantic association deficiency in a traditional method are solved, through lightweight model compression and TensorRT acceleration, the analysis accuracy is remarkably superior to that of the traditional method, and the method can be widely applied to the fields of constructional engineering, petrochemical engineering and the like and has wide application prospects. And the drawing information processing efficiency and the data integrity are improved.

The invention discloses a multi-dimensional train derailment monitoring method based on vision and vibration, and belongs to the field of rail transit, the method comprises the following steps: respectively collecting a wheel-rail contact area image and vibration data of each wheel in different directions, and carrying out space-time alignment on the wheel-rail contact area image and the vibration data; combining an edge detection algorithm and a Hough transform technology to extract visual features from the wheel-rail contact area image, extracting vibration features from the vibration data, and associating the visual features with the vibration features to generate a wheel flange transverse position-vibration feature mapping table; and establishing a D-S evidence theory fusion model based on the wheel flange transverse position-vibration feature mapping table, fusing the visual features and the vibration features by using the D-S evidence theory fusion model, and judging the train derailment risk according to a feature fusion result. According to the invention, deep coupling analysis of visual images and vibration data is realized, and the defects that the prior art cannot adapt to working condition changes, false alarms are easily generated and operation is affected are overcome.

The invention discloses a frequency hopping carrier signal detection method, system and device based on time-frequency analysis and a storable medium, and belongs to the technical field of frequency hopping carrier signal detection. According to the detection method, firstly, a sampling signal is subjected to combined time-frequency analysis transformation (STFTamp; according to the method, a to-be-detected time-frequency graph is obtained through a WVD method, structural elements of different scales are constructed through self-adaptive morphological filtering to process the time-frequency graph, noise and burst signals are removed, influences on to-be-detected frequency hopping signals are avoided, and the detection accuracy is improved. And then, aiming at the residual fixed-frequency signal and sweep-frequency signal interference, Hough transform is used for carrying out feature extraction on a straight line, and a frequency hopping signal is judged according to different time-frequency features of different signals, so that the detection process of the frequency hopping signal is completed. The detection method can be applied to a wireless sensor network and is used for realizing data communication and information transmission between sensor nodes. Through detection and analysis of the frequency hopping signal, the communication stability and reliability of the sensing network in a complex electromagnetic environment can be effectively improved.

The invention discloses a structure large displacement estimation method based on Canny-Hough transformation and KLT optical flow, and relates to the related field of signal processing and vision measurement, and the method comprises the steps: collecting and preprocessing structure vibration data; carrying out integer pixel displacement estimation on the basis of Canny-Hough transformation; reconstructing the video; performing sub-pixel displacement estimation based on the KLT optical flow; and physical displacement. According to the method, the limitation of a single identification method in identifying different pixel levels (pixel level and sub-pixel level) is overcome, the measurement precision of the large displacement of the structure is remarkably improved, and an estimation error caused when an image pyramid is used for large displacement tracking is avoided. The research provides a new target-free large-displacement motion estimation scheme for structural vibration measurement based on computer vision, and has important reference value.

The invention relates to an object orientation detection method and device, computer equipment and a storage medium, which are used for solving the problem of poor accuracy and reliability of object pose adjustment in an existing industrial assembly line. The method comprises the following steps: carrying out image acquisition on a target object through a super-resolution full-spectrum imaging device to obtain a plurality of original image sets; carrying out image noise removal, enhancement and edge detection on the image set to obtain a preprocessed image set; inputting the preprocessed image set into a sparse self-encoding convolutional neural network for feature extraction to obtain object orientation feature data; identifying key feature points of the target object by using improved generalized Hough transform; and estimating the orientation of the object based on a fuzzy logic orientation discrimination algorithm, and generating an operation strategy. Through deep learning feature extraction and fuzzy logic orientation discrimination, accurate detection of the object orientation in a complex industrial environment can be realized, and the automation level and stability of assembly line work are improved.

The invention relates to an intelligent tree barrier detection method and system based on multi-modal perception, and the method employs an unmanned plane to carry a sensor to collect data, including RGB images, LIDAR point clouds, multispectral data and the like. Firstly, data is preprocessed to form standardized input. Hough transformation is carried out on the LIDAR point cloud, the position of the power line is extracted, and a three-dimensional line model is generated. And then positioning a tree bounding box from the RGB image by using a deep learning algorithm, and screening a high-risk point cloud region in combination with a line model. And constructing a three-dimensional tree model, calculating the minimum safety clearance distance between the power line and the tree in real time through a collision detection algorithm, obtaining the three-dimensional tree model with a safety distance mark, and finally carrying out space risk assessment based on the model. According to the invention, the reliability and efficiency of power line inspection can be significantly improved.

The invention relates to an unmanned aerial vehicle autonomous inspection method based on an AI technology, and the method comprises the following steps: S1, capturing a high-resolution image through an airborne camera in an inspection process, carrying out the spatial calibration of the image through IMU and LiDAR data, and guaranteeing the precise correspondence between the captured image data and a physical environment; s2, extracting texture features in the image by using a Gabor filter, acquiring edge features in the image by using a Canny edge detection algorithm, and detecting a linear structure by using Hough transform to assist in identifying a lead; s3, an improved DeepLab model is adopted to segment a lead area; s4, performing real-time tracking on the identified lead position by using a Kalman filter, and introducing a dynamic Bayesian network DBN to perform short-term change prediction on the lead position; and S5, generating a map of the current environment based on SLAM, and optimizing the inspection path of the unmanned aerial vehicle based on an RRT improved algorithm. Accurate wire inspection is achieved, it is ensured that operation is conducted along a safe route of a wire, efficiency is improved, and the risk of inspection is reduced.

The invention provides an automobile part visual inspection correction method and equipment. The method comprises the following steps: extracting a part contour through edge detection, calculating an inclination angle by adopting Hough transform, executing affine transform on an original image, enabling a rotation angle to be negative to the inclination angle, and ensuring that the parts are horizontally aligned; in the corrected horizontal image, calculating a transverse offset through a feature matching algorithm or template matching, and translating an image area to enable the center of the detection window to be aligned with the actual position of the part; if inclination and offset are detected at the same time, inclination correction is executed preferentially, the offset is calculated based on the image after inclination correction, synchronous correction is achieved through a composite transformation algorithm, and accumulative errors are reduced through iterative optimization; and extracting a size parameter from the corrected image, comparing the size parameter with a predetermined tolerance range, and if the measured size is outside the predetermined tolerance range, marking the image as a defective product, and removing the defective product. The method is used for upgrading the visual inspection system for automobile manufacturing.

The invention discloses an AGV-oriented tray dynamic identification and positioning method, and the method comprises the steps: firstly collecting a tray image training model, deploying a weight, and laying an image identification foundation; the RGB-D camera synchronously collects multi-modal images in real time and stores the multi-modal images in a buffer area, so that the data timeliness is guaranteed. And calling a CUDA acceleration model to extract a tray ROI region from the latest RGB image. The depth map is aligned to an RGB coordinate system and a local point cloud is generated. And segmenting the point cloud of the front plane of the tray through RANSAC and projecting to generate a two-dimensional image. And extracting the pixel coordinates of the key points of the contour of the tray based on Canny and Hough transform. And back-projecting the pixel coordinates of the key points to a three-dimensional space to calculate the pose. And the pose information is transmitted to the AGV motion control module. According to the method, the tray pose is accurately obtained, and efficient track adjustment of the AGV is assisted. And carrying out dynamic tracking and accurate positioning on a tray target. Moreover, the AGV can obtain the real-time positioning data of the target in the movement process, thereby dynamically adjusting the track, and effectively solving the problem of accumulative errors of a conventional static positioning mode.

The invention relates to a sun meridian detection and course angle real-time calculation method based on polarized light field vector characteristics, and aims to solve the problems that a traditional sun meridian detection method is easily interfered in low signal-to-noise ratio scenes such as haze and cloud layer shielding, lens distortion causes polarization angle errors, the false detection rate is high, and the calculation accuracy is high. In order to solve the problems that the real-time performance and robustness of course calculation are affected due to the fact that single polarization angle information is difficult to fully reflect the sun direction, polarization parameter calculation is carried out by utilizing a multi-channel polarization image, and the sun meridian is detected by adopting Hough transform through fisheye lens correction, bilateral filtering and candidate region screening, so that the real-time performance and robustness of course calculation are improved. And a multi-point inversion method is combined to accurately obtain a sun vector and a zenith angle and an azimuth angle thereof, and finally a course angle is calculated in real time. The problems of imaging distortion and insufficient real-time processing in the traditional sun positioning technology can be effectively solved, and a powerful navigation positioning technical guarantee is provided for the fields of unmanned aerial vehicles, autonomous navigation, polarized light navigation and the like.

The invention is suitable for the technical field of cross fusion of three-dimensional scene reconstruction and wireless channel modeling, and provides an indoor scene wireless channel modeling method based on laser radar perception reconstruction, which comprises the following steps: firstly, collecting indoor scene point cloud data by using a laser radar, then carrying out noise reduction sampling processing on the point cloud data, and obtaining a noise reduction sampling result; then, structural elements including walls, ceilings and floors are extracted based on random sampling consistency and a Hough transform algorithm, a wall opening structure is detected by adopting an image semantic segmentation model aiming at internal elements, furniture reconstruction is achieved based on template deformation, three-dimensional surface reconstruction is achieved by utilizing a surface element subdivision algorithm, and the wall opening structure is obtained. And finally, matching electromagnetic parameters on the surfaces of different materials, and completing wireless channel modeling through ray tracing simulation. Therefore, a complex scene is divided into structural elements and internal elements, and corresponding algorithms are designed for different elements to extract key geometric features, so that the precision and robustness of reconstruction are improved.

The invention provides an electric power line defect detection method and system based on visual identification, and relates to the technical field of line detection.The method comprises the steps that firstly, a visible light and infrared image dual-light registration and differential operation technology is adopted, and a fusion feature map capable of reflecting component thermal anomaly and material difference at the same time is generated; secondly, a black box type target detection model is abandoned in a part positioning link, but line segment screening and reconstruction are carried out by combining probability Hough transform with specific prior geometric knowledge of a power line, so that dependence on a large amount of labeled data is reduced, interpretability of a positioning process is enhanced, and the positioning accuracy is improved; according to the method, accurate areas of key components such as wire insulators can be extracted in a complex background, the concept of a probability saliency map is introduced in a defect identification core link, so that a defect area is enhanced and highlighted, and then a complete defect contour is determined by adopting an adaptive threshold segmentation and area growing algorithm; accurate mapping from pixel-level features to object-level defects is realized.

The invention discloses an automatic steel coil forklift control method based on image recognition, and the method comprises the steps: obtaining a steel coil surface image, removing corrosion and oil contamination interference through an image enhancement algorithm, and obtaining a clear steel coil surface image; for a clear steel coil surface image, edge feature points are extracted, a Hough transform algorithm is used for detecting the steel coil contour, and the position of the axis of the steel coil is determined; calculating an included angle between the axis of the steel coil and the horizontal plane according to the position of the axis of the steel coil to obtain a multi-dimensional inclination angle component; a three-dimensional reconstruction algorithm is adopted to map the axis position and the inclination angle component of the steel coil to a three-dimensional space coordinate system, and the actual space position of the steel coil is obtained; and according to the actual spatial position of the steel coil, the adjustment amount of the advancing direction and height of the forklift is calculated in combination with a preset forklift path, and a dynamic path planning result is generated.

The invention provides a subway station room layout drawing wall line extraction method based on an improved Canny algorithm and Hough transform. According to the method, through image multi-channel CLAHE processing enhancement and Laplacian sharpening, an improved Canny edge detection algorithm and Hough transform are applied to detect wall lines, then wall line merging is carried out, wall line geometric data of a standardized subway station room layout drawing are extracted, an output file can be directly imported into CAD software, good practical engineering application adaptability is achieved, and the method is suitable for popularization and application. The problems that a traditional method is low in efficiency, poor in precision and unstructured in output are solved.

The invention discloses an anti-interference visual detection method and system for a welding groove edge position of a double-YOLO model, and the method comprises the steps: collecting n frames of welding region global images when an electric arc is located at different positions of a groove, building a YOLO-I model which outputs the welding region global images containing a plurality of welding feature position frames, the method comprises the following steps: acquiring m feature images which comprise different sizes, shapes and numbers of welding interferences and comprise groove edges, establishing a YOLO-II model which outputs the feature images of the groove edges which comprise interference area position information, generating groove left and right edge ROI images through the YOLO-I model, and identifying the groove left and right edge ROI images through the self-adaptive feature identification capability of the YOLO-II model. Welding features in a global image of a welding area or welding interference information such as welding spatter, welding fume and welding arc light in a groove edge ROI image are effectively recognized, and a groove edge line is reconstructed through a linear principal component analysis method based on Hough transform. And the influence of interference factors in the welding process on the detection precision of the edge position of the groove is obviously reduced.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention relates to a circuit diagram-to-netlist generation method and system based on deep learning and a product. The circuit diagram-to-netlist generation method comprises the following steps: S1, identifying devices in a circuit diagram through a YOLOv8 model to obtain position information and category of each device; identifying a straight line segment in the circuit diagram through a probabilistic Hough transform algorithm to obtain a starting point coordinate, an end point coordinate and an orientation corresponding to the straight line segment; and S2, generating corresponding text description information according to the position information, the category, the starting point coordinate, the end point coordinate and the orientation. According to the method, by effectively integrating multi-modal information and combining image and text data, comprehensive understanding of a circuit diagram is provided, the recognition accuracy and robustness are improved, even if a single component has a recognition error, the influence of noise on the whole model can still be reduced through final information fusion, and the recognition accuracy and robustness are improved. Therefore, misjudgment and missed judgment in recognition are reduced, and the recognition accuracy of the element and the connection relation is remarkably improved.

The invention relates to a multi-element track initiation method and system based on modified Hough transformation, and belongs to the technical field of radar information processing. The method comprises the following steps: acquiring all plots after plot condensation, selecting continuous three-beat measurement data on a period, and carrying out one-by-one traversal association to obtain an undetermined track; setting a multi-element constraint condition, screening the to-be-determined track based on the multi-element constraint condition, and reserving the to-be-determined track meeting the screening requirement; removing non-collinear to-be-determined tracks based on Hough transformation to obtain approximately linear to-be-determined tracks in a three-dimensional space; and evaluating the confidence coefficient of the track initiation based on the signal-to-noise ratio of the track, judging the track meeting the signal-to-noise ratio threshold condition as an effective track, and taking the effective track as final output to complete the track initiation. According to the method, on the basis of effectively inhibiting the false track, the rapid track initiation capability of the target is improved, and meanwhile, the problems that the calculation amount of a traditional Hough transform algorithm is too large and the real-time performance is poor are solved.

The invention discloses a digital watermark registration positioning method based on linear Hough transform, which is applied to positioning and identification of a digital watermark on a digital image, and comprises the following steps: encoding input data, adding random offset, generating basic unit watermark blocks, paving the basic unit watermark blocks, and reserving intervals for a linear detection positioning algorithm. In the extraction algorithm, firstly, preprocessing and a Hough straight line detection algorithm are carried out on a carrier image, detected straight lines are grouped, intersection point information between two groups is calculated, vertex positions of basic unit watermark blocks are positioned by calculating the mutual position relation between intersection points, and then perspective transformation correction is carried out on each basic unit watermark block. And the embedded basic unit watermark block information is obtained. And then extracting coding information from the basic unit watermark block, and sequentially carrying out mask removal, error correction and decoding to recover original embedded information. The invention further discloses a system and application for implementing the method, and the system and the application have wide application value.

The invention relates to a vision-based crossing container truck deadbolt state detection method, and belongs to the technical field of image processing. The crossing container truck deadbolt state detection method based on vision comprises the steps that an image of a lock hole area is obtained through a long-focus camera on a mechanical arm, and in order to judge the opening and closing state of a lock pin, edge features of the lock hole and the lock pin need to be extracted; based on the edge strength and in combination with keyhole priori knowledge, carrying out deadbolt state optimization judgment; lock pin state confirmation, result output and linkage control are carried out, a detection result is directly linked with a crossing control system to realize safety management and control of the container truck, and if the lock pin is in a locked state, the system forbids the container truck to pass through the crossing handrail and keeps closed; the container truck lock pin state detection method based on machine vision solves the problem that in an existing container truck lock pin state detection method based on machine vision, the detection precision of Hough transform is low under complex conditions such as uneven illumination, noise interference, lock pin contamination and slight deformation, and is used for improving the accuracy of container truck lock pin state detection.

The present invention relates to an intelligent tree barrier detection method and system based on multimodal perception. The method uses sensors carried by drones to collect data, including RGB images, LIDAR point clouds, multispectral data, etc. First, the data is preprocessed to form a standardized input. Then, a Hough transform is performed on the LIDAR point cloud to extract the position of the power line and generate a three-dimensional line model. Then, a deep learning algorithm is used to locate the tree bounding box from the RGB image, and the high-risk point cloud area is screened in combination with the line model. Then, a three-dimensional tree model is constructed, and the minimum safe clearance distance between the power line and the tree is calculated in real time through a collision detection algorithm to obtain a three-dimensional tree model with a safety distance annotation. Finally, a spatial risk assessment is performed based on this model. The present invention can significantly improve the reliability and efficiency of power line inspections.

The invention discloses a wafer six-surface detection automatic focusing method comprising the following steps: correcting the crystal grain position through a feeding XYR platform, and obtaining a to-be-detected crystal grain image; according to a to-be-detected crystal grain image, edge points in the edge contour of the crystal grain are converted into geometric parameters by using a Hough transform algorithm, and a position compensation value of the crystal grain is calculated; determining an optimal focus position of a front / back detection station of the to-be-detected crystal grain according to the optical characteristics, the light deflection angle and the incident light intensity of the crystal grain, and adjusting the focus position of the focusing module; according to the position compensation value of the crystal grain, an electric sliding table is controlled to carry an optical camera to move in place and focus and image; and by comparing actual detection data with prediction results of the refraction deviation correction model and the crystal grain front / back focus adjustment model, the accuracy of the model is evaluated and optimized. According to the invention, the problems of position error, optical characteristic difference, refraction deviation and the like in the prior art are effectively solved, and efficient and accurate crystal grain detection is realized.