858 results about "Surface map" patented technology

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention relates to the technical field of industrial automatic quality inspection, and provides a PCBA surface defect detection method and system based on deep learning and a medium, and the method is used for carrying out defect detection on a preset PCBA board. Comprising the following steps: acquiring a surface image of a PCBA board according to a preset multi-angle light source and a high-resolution camera, and performing adaptive illumination compensation and noise removal processing on the surface image to generate a standardized image; performing multi-scale segmentation on the standardized image to obtain image blocks including local details and a global structure; constructing a double-branch deep learning model, wherein the double-branch deep learning model comprises a backbone network, a multi-scale feature fusion module and a defect detection branch; inputting the image blocks into a double-branch deep learning model, and outputting a thermodynamic diagram and probability distribution by the double-branch deep learning model; performing binarization processing on the thermodynamic diagram by using a dynamic threshold segmentation algorithm to generate a defect mask; and outputting a defect detection result of the PCBA board according to the defect mask and the probability distribution, and completing the defect detection of the PCBA board.

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

The invention relates to the technical field of grinding, in particular to a self-adaptive force control grinding system and method for a complex curved surface machining robot. Three-dimensional point cloud data of a workpiece are obtained through machine vision, gridding processing is carried out, and normal vector and curvature characteristic parameters are calculated; performing operation area division and operation path planning based on curvature characteristics; calculating operation parameters according to the normal vector and curvature characteristics; a self-adaptive impedance control algorithm is adopted, the polishing force is monitored in real time through a force sensor, and the rigidity and damping parameters of the robot are dynamically adjusted; and performing defect identification on the surface image of the processed workpiece by using the deep learning model, and when defects are identified, re-planning the operation path. According to the method, self-adaptive grinding of the complex curved surface workpiece is achieved, and the grinding efficiency and quality of the complex curved surface can be improved.

The invention relates to the technical field of intelligent sorting, and discloses a multi-mode defect feature fusion intelligent sorting method and system, and the method comprises the steps: obtaining and preprocessing a surface image, infrared thermal imaging and voiceprint vibration data of an object; analyzing and generating multi-modal defect feature parameters, fusing to form a fused feature vector set, and constructing a defect detection reference set; performing dynamic matching verification on the multi-modal data based on the reference set, and analyzing the mismatching state of the surface texture and the thermal distribution by using a space alignment technology; and generating a defect form deviation degree index according to a verification result, and judging whether a sorting action is triggered or not. The system comprises a multi-modal acquisition module, a feature fusion modeling module, a form association verification module and a sorting judgment module which are used for respectively realizing data acquisition preprocessing, feature fusion modeling, cross-modal association analysis and sorting decision. Through multi-modal data fusion and cross-modal quantitative analysis, the comprehensiveness of defect detection and the sorting accuracy are improved.

The invention relates to an intelligent vacuum coating quality monitoring method based on artificial intelligence, which comprises the following steps: collecting process parameters in real time through a coating equipment sensor, dynamically monitoring vacuum degree change, deposition rate deviation and temperature gradient, and standardizing data flow to obtain a quantitative trend curve of process parameter fluctuation; combining the defect microcosmic feature description set with gas flow fluctuation and power supply voltage jitter to obtain a correlation analysis result of process parameters and defect forms, and calibrating defect types according to a defect feature library to obtain an updated defect classification basis; and inputting real-time new surface image data through the updated defect classification basis, performing defect detection for illumination reflection differences and stripe deflection angles, obtaining a preliminary defect classification result, and adjusting classification weight parameters.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention relates to the technical field of industrial product surface quality detection, in particular to a flexible display module surface defect image recognition method, which comprises the following steps: acquiring a plurality of surface images of a flexible display module under different light sources and carrying out distortion removal processing on the surface images; reconstructing and generating three-dimensional reference point cloud data representing the current curved surface form of the module; re-projecting the distorted image to the ideal rigid plane according to the relationship, generating a plurality of corrected images, and generating a plurality of corrected images to eliminate geometric and luminosity distortion introduced by flexible deformation; obtaining a defect candidate area binary image; and extracting a multi-dimensional feature vector of the defect candidate region from the binary image of the defect candidate region, and classifying the feature vector by using a pre-trained defect classification model to obtain a defect identification result. Through the three-dimensional reference point cloud reconstruction and image re-projection technology, the problem of geometric distortion caused by surface deformation of the flexible display module is effectively solved, and misjudgment and missed judgment are avoided.

The invention relates to the technical field of quality detection, in particular to a product quality control method and system based on machine vision, and the method comprises the following steps: obtaining product surface image data, calculating the gray gradient value of each pixel, extracting the gray gradient change rate, recording the gradient amplitude and direction information, and generating product surface gradient data. According to the method, through pixel-level gray scale gradient calculation, the product local feature expression ability is improved, multi-scale gradient change trend analysis is combined, the accurate recognition ability of a product defect area is improved, through texture direction angle calculation and vector field construction, the direction change anomaly detection reliability is enhanced, and the direction change anomaly detection accuracy is improved based on the combination of a direction deviation accumulated value and an abrupt change threshold value. Effective identification of a structure sudden change area is ensured, adjustment is carried out for curvature continuity abnormal points, defect boundary fitting precision is optimized, defect area internal gradient distribution and boundary feature comparative analysis are carried out, accurate classification of defect types is realized, and stability and adaptability of automatic product quality detection are ensured.

The invention discloses an image analysis method and system for concrete apparent quality defect detection, and relates to the technical field of concrete apparent quality defect detection.The method comprises the steps that a camera device is used for collecting concrete surface images in a specified distance interval, and the optical axis of a camera lens is controlled to be perpendicular to the concrete surface; carrying out image preprocessing on the collected concrete surface image, and carrying out defect type image marking; carrying out key feature extraction on the preprocessed concrete surface image by adopting a convolutional neural network to obtain key feature information; constructing a multi-algorithm comparison verification framework, performing cross validation in combination with the key feature information to obtain defect detection results and evaluation results of a plurality of defect detection models, optimizing model parameters in combination with a confusion matrix, and determining a final concrete surface detection model; obtaining an apparent quality defect detection result based on the defect detection result and a preset quality defect grading threshold value; the efficiency of concrete apparent defect detection is improved.

The invention provides a road surface defect detection method and system based on multi-sensor fusion, and the method comprises the steps: 1, capturing a road surface image through an RGB camera, and obtaining the three-dimensional point cloud data of a road surface through LiDAR; 2, performing feature extraction on the road surface image captured by the RGB camera by using a YOLOv5 network; step 3, processing the road surface three-dimensional point cloud data acquired by the LiDAR by using a Point Net + + network; step 4, carrying out deep fusion on the image features extracted by the YOLOv5 and the point cloud features extracted by the Point Net + +; and 5, detecting and classifying the road surface defects by adopting a quantitative and qualitative combined method. According to the method, an RGB camera and LiDAR (light detection and distance measurement) are adopted, a YOLOv5 network is combined with PointNet + +, high-precision and real-time detection and classification of road surface defects are achieved, and the defects of a traditional method are overcome.

The invention provides an aluminum profile defect analysis method and system based on texture features, and relates to the technical field of edge detection, and the method comprises the steps: obtaining an aluminum profile surface image, and determining an extrusion direction; dividing the image into a plurality of local blocks, carrying out gradient direction and amplitude calculation on each block, and judging whether the block belongs to a high-confidence-coefficient texture region or not by combining a direction difference value and confidence coefficient; a first suppression coefficient is executed on the high-confidence-coefficient texture region for primary reduction to form a first processing image, then the updated image is judged again, a second suppression coefficient with higher strength is applied to the region still having obvious texture features, and a second processing image is generated; and finally, defect identification is carried out on the weakened image through edge detection, and residual texture false edges are removed in combination with direction consistency or connectivity analysis. The method has the advantages of light weight, low computing power consumption and high accuracy, and can be applied to online detection and quality control of the surface flaws of the aluminum profile in industrial production.

The invention relates to the technical field of cloth flaw detection and deep learning, in particular to an MAC-YOLO-based cloth flaw detection method, which comprises the following steps of (1) constructing an MAC-YOLO network, and improving the MAC-YOLO network based on a YOLOv8 network; (2) collecting a cloth surface image data set, carrying out defect type labeling on defect images, and randomly dividing the data set into a training set and a test set according to a proportion of 8: 2; (3) the training set is used for training the MAC-YOLO network, an AdamW optimizer is adopted, a learning rate strategy is adjusted in stages, and a loss function comprises binary cross entropy loss of classification branches and distribution focus loss and complete intersection-to-union ratio loss of regression branches; and (4) detecting the input cloth surface image by using the trained MAC-YOLO network, and outputting the category and position prediction map of the flaws.When the extreme length-width ratio image data is processed, various flaws can be accurately detected.

The invention relates to the technical field of defect detection, in particular to a heat-conducting adhesive tape defect detection method and system based on machine vision, and the method comprises the following steps: obtaining a surface image of a heat-conducting adhesive tape, extracting a texture trend and marking an aggregation block, adjusting the trend and offset of a connecting line segment, and analyzing the angle change of a sideband node. And recognizing a skeleton trend reverse section and closing a boundary, and extracting a continuous direction changing region to obtain a defect structure image annotation layer. According to the method, the turning position in the sideband region is extracted by constructing the direction aggregation graph of the texture aggregation block, enhancing the direction identification of the direction of the regional texture, combining the grouping organization and coordinate offset processing of the line segment path, enhancing the continuous association of the image structure, and by means of the repeated distribution characteristic of the angle mutation node; and through a skeleton trend reverse paragraph and boundary closing mode, an identification range of a structure disturbance block is expanded, and coherent tracking and multi-level labeling of a structure abnormal region are completed by combining a spatial relationship between a contour trend difference and a turning point.

The invention relates to the technical field of photovoltaic cleaning control, and discloses a spraying cleaning control method of a photovoltaic cleaning unmanned aerial vehicle. The method comprises the following steps: receiving photovoltaic panel surface state data and environmental parameters, comparing dirt grade standards to obtain dirt distribution characteristics and stubborn stain positions, and determining initial spraying parameters through a self-adaptive control algorithm in combination with the environmental parameters; after the spraying executing mechanism is driven to conduct preliminary cleaning, an image collecting device is used for obtaining surface image data, and a deviation value is calculated by comparing a cleanliness threshold value; and in combination with flight attitude data of the unmanned aerial vehicle, a fuzzy decision system is adopted to dynamically adjust initial spraying parameters to generate a real-time control instruction, an execution mechanism is controlled to perform fine cleaning until surface image data accords with a cleanliness threshold, and a final scheme is generated. According to the method, through real-time feedback and dynamic adjustment, spraying parameters are made to adapt to different dirt states and environment conditions, targeted cleaning is achieved, and the intelligence and accuracy of photovoltaic panel cleaning are improved.

The invention discloses an intelligent PCB sorting process and system based on an unmanned factory, and the process comprises the steps: obtaining surface images and internal structure parameters of PCBs in real time through an image collection module and a scanning module on sorting equipment, and generating a standardized feature data set; the lightweight classification model performs classification and quality grade division on the PCBs in combination with a process grade judgment rule, and dynamically allocates sorting priorities based on MES system order demands to generate a task queue; the path planning algorithm calculates the optimal grabbing path of the mechanical arm and the AGV conveying path according to the priority queue, and a sorting instruction set is formed and issued to the execution unit through the industrial communication network; the mechanical arm completes PCB grabbing and placing operation according to the instruction, and meanwhile, the visual verification module detects a sorting result in real time and outputs a sorting state report and quality data; according to the process, high efficiency, accuracy and unmanned operation of PCB sorting are realized through automatic data acquisition, intelligent classification decision and a closed-loop optimization mechanism.

The invention provides a concrete bridge damage detection and residual life prediction method based on machine vision. The method comprises the following steps: acquiring a concrete bridge surface image and a corresponding structure coordinate thereof; the structure coordinates are explicitly embedded into the concrete bridge surface image, and a coordinate surface image is obtained; the coordinate surface image is input into an image recognition model, image types and corresponding structure coordinates are recognized, the image types comprise an extrinsic damage type image and a mark type image, and the mark type image is a mark image generated by manual testing; determining each first life influence parameter according to the external display damage image and the corresponding structure coordinate; determining each second life influence parameter according to each mark type image and the corresponding structure coordinate; and estimating the remaining life of the concrete bridge according to each first life influence parameter and each second life influence parameter. By implementing the method, the comprehensiveness of concrete bridge damage detection and the accuracy of residual life prediction can be improved.

Techniques are provided comprising receiving map data and determining a first polygon representing a first traversable surface and a second polygon representing a second traversable surface. The first polygon may include a first portion that overlaps with a second portion of the second polygon. The techniques may determine, based at least in part on elevation information associated with the first traversable surface and the second traversable surface, respective elevation layers associated with the first polygon and the second polygon. It may be determined whether to merge the first polygon and the second polygon based at least in part on whether the first polygon is associated with a same elevation layer as the second polygon. Based at least in part on determining whether to merge the first and second polygons, an updated map may be determined.

The invention provides a power adapter appearance quality detection method and system based on machine vision, and particularly relates to the technical field of power adapter appearance quality detection.The method comprises the steps that a main control module controls a light source module to output an illumination condition matched with a surface material of a power adapter, and an industrial camera is triggered to collect a surface image; preprocessing the image to generate preprocessed image data; inputting the preprocessed image data into a defect identification model to extract defect features and generate a defect classification result; if the classification result contains the reflective interference mark, obtaining material information through an infrared sensor, adjusting light source parameters, and re-collecting and processing the image; otherwise, generating a quality detection signal according to a classification result, and transmitting the quality detection signal to a classification execution mechanism to separate the defective power adapter. According to the method, self-adaptive high-precision detection on a low-cost embedded hardware platform is realized, and the efficiency and the adaptability are improved.

The invention provides a digital evaluation method and system for fair-faced concrete based on Mockup, and belongs to the field of building construction. According to the technical scheme, the method comprises the steps of collecting a surface image of a bare concrete Mock sample plate, extracting a reference Lab color value and a reference texture feature from the collected sample plate image, and constructing an evaluation reference model; collecting a surface image of the to-be-evaluated bare concrete member, and generating image data; performing defect area identification on the image data, extracting an effective image area, and extracting a Lab color value to be evaluated and a texture feature to be evaluated from the effective image area; generating a comparison result based on the evaluation reference model; and based on the comparison result, generating evaluation output information. The method has the beneficial effects that the color and texture double-feature model is constructed, a defect identification and shielding mechanism is introduced, and a structured comparison algorithm and an output system are adopted, so that the automation of the whole process from data acquisition, feature extraction, defect avoidance to intelligent comparison and evaluation output is realized.

The invention discloses a production quality evaluation method for a gold bonding wire, and relates to the technical field of microelectronic packaging, and the method comprises the following steps: obtaining a surface image of the gold bonding wire through a high-resolution image collection system; performing preprocessing on the image data based on the acquired image to eliminate noise and enhance contrast; through the image data, extracting surface topography gradient features by using morphological operation, and quantifying microstructure changes of the surface of the gold wire; extracting texture energy characteristics through wavelet multi-scale decomposition, and analyzing distribution characteristics of surface textures from different scales; self-adaptive fusion is carried out on the morphological gradient features and the texture energy features, and the feature expression of the defect area is enhanced by dynamically adjusting the weight; dividing the boundary of a defect region by adopting an adaptive threshold segmentation method, and separating a normal region from an abnormal region; and extracting geometric and textural features of the defect region obtained by segmentation, realizing automatic discrimination of defect types through a classification model, and outputting a quality evaluation result.

The invention discloses a machine vision-based wood surface defect online detection method and system. The method comprises the following steps of: acquiring surface image data of wood, performing noise reduction on the data by utilizing adaptive median filtering, eliminating surface texture interference based on curve fitting, and performing multi-scale feature extraction on initial wood surface image data by utilizing an improved ResNet-18 network; and through weighted fusion of multispectral channels, extracting difference features of the wood surface, calculating an adaptive threshold segmentation defect area based on a gray average and a standard deviation of initial wood surface image data, performing optimization by using a Canny operator and morphological operation, and identifying segmented image data and a three-dimensional feature matrix by using a YOLOv8s model. And classifying and positioning the defects. According to the method, rapid and accurate classification and positioning of various wood defects are achieved, and compared with a traditional detection method, the detection efficiency and the detection accuracy are improved.

The invention provides a visual detection device and method for building engineering wall quality, and the method comprises the steps: firstly obtaining a multi-angle image set of a to-be-detected wall, which comprises surface images and internal structure scanning images under different illumination conditions, and then carrying out the visual feature extraction of the multi-angle image set, according to the method, a comprehensive visual detection feature set covering surface integrity, internal crack distribution and material uniformity features is obtained, defect area positioning processing is performed on the comprehensive visual detection feature set based on a preset quality evaluation rule, a defect distribution map is generated, and then a visual quality report is generated according to the defect distribution map. And finally, the visual quality report is transmitted to terminal equipment to trigger wall maintenance operation, so that comprehensive and accurate detection and visual presentation of the wall quality are realized, wall maintenance can be effectively guided, and the construction engineering wall quality guarantee level is improved.

The invention relates to the technical field of digital image correlation DIC measurement, in particular to a DIC displacement field dynamic correction method and system based on a lightweight convolutional network, and the method comprises the steps: 1, collecting a structure surface image sequence, and carrying out the preprocessing; 2, calculating an initial displacement field of the preprocessed image sequence through a traditional DIC algorithm, extracting key feature information, and taking the key feature information as input data for dynamic correction; 3, constructing a lightweight convolutional neural network model, transmitting the extracted input data to the lightweight convolutional neural network model for recognition, designing a mixed loss function for training, and learning a mapping relation between an initial displacement field and a real displacement field; and 4, correcting the initial displacement field through the trained lightweight convolutional neural network model, and directly outputting corrected high-precision displacement field data. By using the function fitting capability of the neural network, high-precision displacement field correction is realized under limited computing resources, and the measurement precision is improved.

The invention relates to the technical field of image data processing, in particular to a method for detecting the surface quality of a mining anchor cable steel strand after stabilization treatment, and the method comprises the steps: obtaining a surface image of a to-be-detected steel strand; determining a local texture collaboration degree index of each pixel point; determining a longitudinal consistency deviation index of each pixel point; determining a defect significance index of each pixel point; and identifying a defect area in the surface image of the to-be-detected steel strand based on the defect saliency index of each pixel point. According to the method, a local texture synergy degree and longitudinal consistency deviation index is constructed, and the gray change characteristics and the spatial change rate are combined, so that multi-dimensional quantitative detection of small defects on the surface of the steel strand is realized, noise and real defects are effectively distinguished, the sensitivity to low-contrast defects is improved, missing detection and false alarm are reduced, and the detection accuracy is improved. And a high-reliability quality detection method is provided for the mining anchor cable steel strand.

The invention relates to the technical field of defect detection, in particular to a mold defect detection method and system based on an image recognition technology. The method comprises the following steps: acquiring a parting surface image of a target mold; performing region segmentation on the parting surface image to generate parting surface contour data; identifying a mold closing gap of the parting surface contour data, and generating a parting surface gap distribution diagram; an ejection mechanism of the mold is positioned based on the parting surface gap distribution diagram, and position coordinates of the ejection mechanism are obtained; controlling an image acquisition device to shoot a local area of the ejection mechanism according to the position coordinates of the ejection mechanism to obtain a local high-definition image of the ejection mechanism; performing ejector pin assembly segmentation on the local high-definition image of the ejection mechanism, identifying the contour of an ejector pin positioning plate and extracting coordinates of a mounting hole; according to the method, high-precision identification and cause analysis of mold defects are realized through precise positioning, refined defect classification and process simulation, and the defects of inaccurate positioning, fuzzy identification and lack of dynamic adaptation in traditional detection are overcome.

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention relates to the technical field of rock mechanical tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain cooperative monitoring, and the method comprises the steps: building a monitoring system comprising acoustic emission and an industrial camera array, and building a unified timestamp of each subsystem through a GPS time service module; through load triggering logic, a synchronous trigger is utilized to synchronously acquire an acoustic emission signal and a sample surface image sequence, and evolution characteristics of three-dimensional coordinates of an acoustic emission source and surface full-field strain data are solved. And on the basis, calculating a main strain field variation coefficient, and performing cross-correlation analysis on the resampled energy rate and strain rate. And finally, according to multi-parameter coupling criteria of acoustic emission time sequence parameters, positioning events and surface strain, identifying rock fracture precursor types, and predicting fracture moments and areas by combining a Voight model inverse velocity method and a seismic source projection technology.

The invention discloses a weldment surface defect detection method and system based on a gray level co-occurrence matrix and YOLOv11, and belongs to the technical field of weldment surface defect detection. According to the technical scheme, the method and system for detecting the surface defects of the welding part based on the gray-level co-occurrence matrix and the YOLOv11 specifically comprise the following steps that S1, an industrial camera collects surface images of the welding part under different defect types and illumination conditions, data enhancement processing is carried out, and data enhancement comprises horizontal overturning, color changing, scaling and noise injection; s2, marking bounding box positions and category labels of defects in the enhanced image; according to the method, the texture features of the gray level co-occurrence matrix and the depth features of the improved YOLOv11 model are fused, the attention mechanism and the multi-scale feature fusion technology are combined, the problems that a traditional method is low in efficiency and poor in adaptability and a deep learning model is insufficient in small target detection capacity are effectively solved, and the method has the advantages of being high in detection precision and high in environmental adaptability.