13005results about "Optically investigating flaws/contamination" patented technology

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention relates to the technical field of material nondestructive testing, and discloses a membrane structure welding seam defect detection method based on image recognition, which is used for solving the problems of low defect segmentation accuracy and incapability of effectively recognizing internal defects caused by continuous image gray change of lap welding seams of unequal-thickness flexible materials in a traditional method. The method comprises the following steps: firstly, collecting an original image of the lap weld of the unequal-thickness flexible material, carrying out gray conversion, analyzing thickness gradient distribution, adjusting a gray value, carrying out region segmentation to lock a weld range, extracting potential defect edge features to form a candidate region, and carrying out classified verification to confirm internal defects. Aiming at the problem of low defect segmentation accuracy caused by continuous change of image gray in the prior art, the method improves the defect identification precision through segmented mapping and boundary tracking logic, and is suitable for membrane structure engineering quality control.

The invention discloses an industrial surface defect detection method based on multi-scale feature fusion, and the method comprises the steps: collecting the multi-source data of a detected surface in real time through a multi-modal sensor array, and forming a structured data set through time-space synchronization and denoising; constructing an adaptive geometric correction model to realize spatial transformation and scale normalization of multi-scale features, and cooperatively realizing cross-modal alignment and preliminary fusion through texture and physical attribute branches of a double-flow decoding network; dynamically reweighting the fusion features based on a defect physical model, strengthening physical mechanism defect characterization and suppressing interference; combining optical flow compensation and three-dimensional convolution to extract spatio-temporal evolution characteristics, and forming dynamic defect characterization; and finally outputting defect type and severity evaluation through the classification model in combination with the process parameter library. Therefore, the adaptability of the method to a complex industrial environment is enhanced, and the detection stability can be maintained under different materials, illumination conditions and dynamic interference.

The invention discloses an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

A method for quality inspection of an etching paste material based on a spectral response difference, including: identifying a position of density mutation boundary through spectral collection and density gradient analysis. Applying a specific frequency beam to obtain the resonance response parameter, generating stress field reconstruction data. Performing a reverse optical tracking to obtain a defect formation path, generating degradation prediction data. Establishing a self-organizing optical monitoring grid, to form a hierarchical spectral fingerprint library. Constructing a multi-point linked defect blocking network, to form an interconnected optical energy field. Monitoring a cooperative response to obtain network stability data, Identifying abnormal position coordinates based on a material quality grade and the network stability data, obtaining the abnormal position coordinates. Performing a phase adjustment to obtain a phase difference spectral set, conducting a phase comparison marking on the abnormal position coordinates, completing the quality inspection of the etching paste material.

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

The invention belongs to the technical field of microwave measurement, and discloses an outer wall hollowing microwave reflection detection method based on multi-modal fusion, which comprises the following steps: carrying out multi-modal scanning on a building outer wall to be detected, obtaining visible light image data and infrared temperature distribution data of the outer wall surface, and carrying out space registration and coordinate mapping; a unified multi-modal fusion data set is formed; performing anomaly screening on the multi-modal fusion data set, and identifying a thermal anomaly region by analyzing infrared temperature distribution data; detecting a bump or crack area in combination with texture and morphology anomaly features of the visible light image data; performing information fusion on the thermal anomaly region and the bump or crack region, and extracting candidate detection regions of suspected hollowing; high-precision recognition and quantitative evaluation of the outer wall hollowing are achieved, and the precision and stability of outer wall hollowing detection are improved.

The invention provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

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 belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention relates to the field of insulator nondestructive testing, and discloses an insulator product surface defect nondestructive testing method based on AI identification, and the method comprises a data acquisition module, a preprocessing module, an AI analysis module, a decision output module, a self-optimization module, and an edge calculation node. Through multi-modal data fusion and a deep convolutional neural network technology, accurate detection of surface defects such as cracks, dirt and damage is realized, the omission ratio and the false detection rate are reduced, and the detection precision is improved compared with the traditional manual inspection efficiency; visible light, infrared thermal imaging, ultrasonic waves and hyperspectral data are combined, the surface and internal defects of the insulator are comprehensively covered, the detection rate of tiny cracks and hidden dirt is increased, and the technical limitation of a single sensor is broken through.

The invention provides a metal product surface flaw detection method and system based on machine vision, and belongs to the technical field of machine vision. The method comprises the following steps: constructing a multi-modal image data set through bright field image acquisition, dark field image acquisition and three-dimensional point cloud data acquisition of the surface of a detected metal product, and registering the multi-modal image data set; based on the registration multi-modal image data of pixel-level alignment, pixel-level defect segmentation is carried out by adopting a double-flow diffusion Transform model, and a defect segmentation map is output; and on the basis of the defect segmentation image and the registered multi-modal image data, through segmentation image binaryzation and region extraction, multi-dimensional defect feature extraction and quantification, defect classification and report generation, a final defect detection report is output, and the metal product surface defect detection method is completed. The invention effectively solves the core pain points of low precision, poor robustness and insufficient generalization ability for unknown defects in metal surface flaw detection, and provides an automatic detection method with high reliability and high precision.

The invention discloses a stainless steel tube surface defect detection method and system. The method comprises the following steps: S1, collecting stainless steel tube scanning images under dark field and bright field illumination; s2, splicing the line scanning images into dark field and bright field cylindrical expanded images, and executing brightness equalization and reflection suppression processing; s3, carrying out pixel-level fusion on the dark field and bright field cylindrical expansion images according to a set weight; s4, inputting the fusion cylinder expansion image into a YOLO network trunk of an integrated Swin Transform module, and extracting a multi-scale feature map; s5, performing feature fusion and bounding box prediction, and outputting a bounding box, confidence and a category label of the defect; s6, performing semantic segmentation, gray segmentation and weighted fusion in the detection frame area to generate a final defect mask; and S7, calculating the area, length, width and centroid coordinates of the defect, and converting the actual size and the spatial position. The stainless steel tube surface defect recognition accuracy and stability are improved.

The invention discloses a visual combined infrared precast beam appearance defect detection method, and relates to the technical field of civil engineering structure detection and nondestructive testing, and the method comprises the following steps: establishing an illumination time sequence observation layer, collecting multi-dimensional optical parameters aiming at the surface of a precast beam, and generating a dynamic reflection fingerprint baseline; and performing coherent phase decomposition based on the dynamic reflection fingerprint baseline, separating the specular reflection signal from the material texture signal, and calibrating light spot track and intensity evolution data in the unified baseline. According to the method, an illumination time sequence observation layer is constructed to generate reflection fingerprints, coherent phase decomposition and anti-fact playback are combined, reflection interference is stripped, and crack boundaries are recovered; time coordinates are reconstructed through double-mirror-image anchor points, and crack evolution is accurately recovered; phase conjugate projection and light field traction are combined, a dynamic threshold optical fence is established, the exposure rhythm is controlled in a closed-loop mode, and precise detection of the defects of the precast beam is achieved.

The invention provides an intelligent pipe surface quality detection method and system based on machine vision, relates to the technical field of industrial automatic detection and machine vision, and aims to establish a pipe surface feature library and mark shape abnormal features. The method comprises the following steps: collecting an image of a pipe in a bright and dark composite light field, extracting gray and texture features after polarization filtering processing, reconstructing a three-dimensional point cloud covering a mortar layer and a concrete layer by matching a principal component analysis dimensionality reduction fusion feature set with a feature library, and converting the point cloud into a two-dimensional expansion graph through cylindrical projection; a mortar abnormal area and a concrete abnormal area are segmented, the hole volume is calculated through point cloud residual errors in the mortar area, and internal hollowing is detected in combination with acoustic vibration excitation and thermal response; the concrete area locates defects based on point cloud features; according to the sequence of the concrete covering process before the mortar covering process, a correlation model is constructed to match and coincide the defect sites, and the detection result is output, so that the detection automation degree and accuracy can be improved.

The invention discloses a multispectral pollution analysis method and system for a photovoltaic cleaning unmanned aerial vehicle, and belongs to the technical field of spectrum detection.The multispectral pollution analysis method comprises the steps that a multispectral image is obtained, dark current-radiation-atmosphere-shadow full-link preprocessing is carried out, multiband weighted threshold segmentation and morphological post-processing are constructed, and a pollution area is extracted; establishing a self-learning pollution spectral feature library, and identifying pollution types by using cosine similarity; generating a pollution degree distribution diagram in combination with the pollution type-efficiency attenuation mapping table; and when the overall efficiency loss exceeds a preset value, planning a three-dimensional track based on a cluster priority score and an ant colony-genetic fusion algorithm, and returning an image to perform closed-loop verification and complementary cleaning after the unmanned aerial vehicle performs cleaning. According to the invention, the pollution identification precision, the efficiency evaluation accuracy and the cleaning intelligence level are significantly improved, and the method can be widely applied to unattended operation and maintenance of the photovoltaic power station.

The invention relates to the technical field of electronic detection, and discloses an electronic component dispensing detection method. The method comprises the following steps: acquiring dispensing image data of the surface of the electronic component, and generating a standardized dispensing data matrix containing glue position, thickness and uniformity characteristics through standardized preprocessing; constructing a dispensing correction matrix based on an adaptive window frame, and performing spatial reference dynamic correction on the standardized data matrix to obtain a spatial correction dispensing data matrix; inputting the data into a multi-layer sensor fusion network for feature fusion, and outputting a multi-source fusion dispensing data set; constructing a multi-dimensional abnormal feature incidence matrix based on the data set, and identifying abnormal dispensing data nodes by using a dynamic threshold detection algorithm; performing parameter optimization iteration on the multi-source fusion data set by using a gradient descent optimization algorithm to generate an optimized dispensing parameter set; and finally, constructing a three-dimensional visual dispensing quality model, and establishing a dynamic mapping relationship between model parameters and glue physical characteristics. The method can more comprehensively detect the glue quality.

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 precision manufacturing nondestructive testing and machine vision image processing, in particular to a titanium alloy ring piece surface microcrack defect detection system based on machine vision, which comprises an image acquisition module for acquiring a to-be-processed image data set; the manifold calibration module is used for acquiring a main direction field of background textures and converting the to-be-processed image data set into a standard space image with aligned texture flow; the sparse decomposition module is used for acquiring a shear wave coefficient and decomposing the shear wave coefficient into a low-rank component matrix and a sparse component matrix; the reconstruction judgment module is used for generating a microcrack defect distribution diagram, obtaining a residual image and generating a final defect distribution diagram; the self-adaptive feedback module is used for adjusting the sparsity constraint weight in the robust principal component analysis algorithm; according to the method, the problem of signal aliasing caused by frequency overlapping of cracks and background textures is effectively solved, and the detection sensitivity under the strong texture background is remarkably improved.

The invention relates to the technical field of metal structure detection, in particular to an intelligent special-shaped aluminum plate structure parameter detection method and system based on spectral analysis, and solves the problems of multi-parameter splitting, poor complex structure adaptability and inaccurate risk assessment in traditional detection. According to the method, data are synchronously collected through multi-spectral imaging and line laser scanning, surface component parameters such as the thickness gradient of an oxide film are inverted, the local curvature is calculated based on three-dimensional point cloud, a spiral encryption or grid sparse scanning track is dynamically switched, differential detection of a complex structure and a plane area is achieved, and the detection precision is improved. And then a space correlation model of the oxidation film thickness and the hole deformation quantity is constructed, a structure failure risk area is accurately recognized by combining a three-level risk judgment rule and cross-regional influence analysis, and the system effectively improves the accuracy and detection efficiency of defect recognition of the special-shaped aluminum plate through cooperation of dynamic scanning control, component deformation correlation and a composite structure decision unit.

The invention discloses a lens production defect detection method and equipment thereof, and relates to the technical field of visual inspection.The equipment comprises an information acquisition module, a central processing unit, a process regulation and control module and an interactive communication module.The information acquisition module acquires multi-source sensing information at regular time, so that both comprehensiveness and accuracy are improved; high accuracy of visual detection is guaranteed; through an information processing model, quantification is accurate and traceable, defect degree visualization and defect accurate classification are realized, then process defects are positioned, the interference degree of the environment on a detection result is determined, and misjudgment of the defects due to environmental factors is avoided; through linkage of defect data, process precision and environmental interference, a process optimization scheme is generated in a targeted manner, closed-loop response from defect generation to process adjustment is realized, and the production line response speed is improved; and finally, the lens production yield is remarkably improved, and the production cost of long-term detection is reduced.

The invention relates to the technical field of mode recognition and data recognition, and discloses an intelligent visual detection method for non-standard precision part surface microdefects, which comprises the following steps: acquiring surface gray level image data of a to-be-detected part, physically abandoning low-frequency components through discrete wavelet transform, and reserving high-frequency detail components to construct a frequency domain input tensor; constructing a double-flow reconstruction model containing a space domain coding network and a frequency domain coding network, and minimizing the distribution difference of the same feature between double-domain characterization through potential feature space consistency constraint joint optimization; the method comprises the following steps of: calculating a spatial domain residual image and a frequency domain residual image, combining a texture topological residual image extracted by structural tensor characteristic decomposition, and generating a comprehensive abnormal response image through weighted fusion to judge the defect, and effectively inhibiting macroscopic geometric contour interference through frequency domain decoupling and a topological check mechanism on the premise of not needing a standard geometric template. And sensitive perception and accurate identification of weak texture defects on the surface of the non-standard part are realized.

The invention relates to the technical field of surface defect detection, in particular to a structure surface disease diagnosis method and system based on multi-modal edge calculation, and the method comprises the following steps: obtaining an infrared thermal image frame image and constructing a temperature difference image group, enhancing visible light texture features to generate an enhanced image group, carrying out image registration, extracting a combined feature vector, and carrying out classification and recognition. And mapping the boundary of the defect area to generate a coordinate set, counting disease information and generating a visual display layer. According to the method, thermal anomaly features in different areas can be visually expressed through temperature mapping and space division operation of the infrared thermal imaging image, accurate judgment of defect types is realized through a trained neural network model, a boundary coordinate point set of structure surface defects is extracted through an image mapping means, and the accuracy of the structure surface defects is improved. And in combination with connectivity operation and a rectangular frame construction mode, defect positions are labeled and positioned, so that the accuracy of building surface disease identification, the reliability of boundary extraction and the integrity of zoning risk presentation are effectively improved.

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 discloses a laser welding seam online quality detection system and method based on multi-mode visual fusion, and particularly relates to the technical field of laser welding seam quality detection. Comprising a data acquisition and synchronous control unit, a 2D image processing and defect detection unit, a 3D point cloud processing and size measurement unit, a multi-modal information fusion and collaborative analysis unit and a comprehensive quality judgment and output unit. According to the laser welding seam online quality detection system and method based on multi-modal visual fusion, the 2D and 3D multi-modal visual fusion technology is adopted, an improved defect detection algorithm and an accurate size measurement method are combined, welding seam surface defects are accurately recognized through 2D images, key size data are obtained by means of 3D point cloud, and the detection accuracy is improved. And the limitation of'heavy defects and light sizes' or'heavy sizes and light defects' in single-modal detection is avoided, and comprehensive and accurate evaluation of the welding seam quality is realized.

The invention provides a plate edge sealing quality detection method and system based on machine vision, and the method comprises the steps: obtaining image data streams continuously collected in a plate edge sealing processing process, carrying out the light intensity change feature extraction of the image data streams, and obtaining a time sequence light variable feature matrix and a space light variable gradient map of an edge sealing region in an edge sealing image frame sequence; carrying out relevance enhancement on the time sequence optical variation characteristic matrix and the spatial optical variation gradient map through a preset characteristic enhancement model, and generating an edge sealing quality characteristic map with a space-time constraint relation; performing defect mode identification based on the edge sealing quality characteristic spectrum to obtain defect types existing in the edge sealing area of the plate and position distribution characteristics of the defects in the edge sealing image frame sequence; and generating a quality optimization instruction containing a parameter adjustment instruction according to the defect type and the position distribution characteristic, and sending the quality optimization instruction to the plate edge sealing control equipment. According to the invention, the overall precision and stability of plate edge sealing quality detection can be improved.

The invention discloses a bridge structure low-altitude inspection and disease assessment system and method based on deep learning, and belongs to the technical field of bridge disease detection and structure health monitoring. The system comprises a multispectral adaptive image acquisition module, a multi-scale disease detection and feature extraction module, a digital twin mapping and disease positioning module and a disease evolution prediction and maintenance decision module. The system dynamically adjusts acquisition parameters according to environmental conditions, accurately identifies diseases of different scales based on a multi-scale convolutional neural network, realizes centimeter-level accurate positioning of the diseases through a three-dimensional digital twinborn model, analyzes a disease time sequence evolution trend and generates graded maintenance suggestions, and realizes adaptive optimization through a closed-loop feedback mechanism. The method is high in environmental adaptability, high in detection precision and accurate in positioning, has disease evolution analysis capability, and provides comprehensive technical support for bridge health monitoring and intelligent management.

The invention relates to the technical field of defect detection systems, and discloses a titanium alloy surface crack defect detection system based on deep learning. According to the system, a crack feature extraction module is used for collecting a titanium alloy surface image and extracting multi-scale crack features including crack trend distribution features and micro-crack density features; the multi-modal data fusion module is used for receiving the multi-scale crack features and performing space-time alignment on the multi-scale crack features and ultrasonic reflection wave features collected in real time to generate a fusion defect feature matrix; the dynamic learning engine module is used for constructing a crack propagation prediction model according to the historical change trend of the fusion defect feature matrix and outputting a dynamic defect response vector; the defect positioning module is used for mapping the dynamic defect response vector to a titanium alloy surface three-dimensional coordinate space to generate a defect position thermodynamic diagram; and the self-adaptive scanning control module is used for analyzing the defect confidence of each area in the defect position thermodynamic diagram and dynamically adjusting the scanning path and the focal length parameter of the industrial camera.

The invention discloses a drainage pipeline defect detection system and method based on multi-scale feature fusion and occlusion perception, and the system comprises a data set construction module which is used for constructing a drainage pipeline data set covering multi-defect, multi-scale and multi-occlusion scenes; the model training module is based on establishment of a defect collaborative detection model, and a backbone network of the model training module adopts a feature pyramid sharing convolution module to reinforce the multi-scale detail extraction capability; the neck network introduces an advanced screening path aggregation network and a selective feature fusion module to realize dynamic feature screening and fusion; the detection head is integrated with an MCFEM module, and shielding perception and scale adaptability are enhanced. According to the method, the problems of high omission ratio and poor robustness caused by large defect scale change, serious shielding and complex background in drainage pipeline detection are effectively solved, the small target defect identification precision and the shielding scene detection accuracy are remarkably improved, and the method is suitable for high-precision and light-weight detection of multi-scale defects in a complex shielding environment.

The invention discloses a rock mass fracture multi-field coupling two-phase flow analysis method and a test system, and relates to the technical field of rock fracture temperature-seepage-stress coupling action mechanism research. The analysis method comprises the following steps: saturating a crack sample with a first dyeing fluid under a target temperature and pressure condition, and obtaining a single-phase saturated flow image of the crack sample; on the basis of the single-phase saturated flow image, through optical iteration inversion calculation, obtaining fracture opening two-dimensional distribution characteristics under a target condition; under the condition of the same temperature and pressure, displacing the first dyeing fluid with the second dyeing fluid or co-flowing with the first dyeing fluid to obtain a two-phase flow image of the crack sample; performing digital image processing on the two-phase flow image and the single-phase saturated flow image, and extracting phase distribution information of the second dyeing fluid; and performing fusion analysis on the phase distribution information of the second dyeing fluid and the fracture opening two-dimensional distribution characteristics, and calculating a two-phase flow structure and saturation distribution characteristics. According to the method, rock mass fracture two-phase flow process quantitative analysis can be realized under different temperature and pressure conditions.