134 results about "Visual defects" patented technology

A visual defect detection method, device, medium and product relate to the field of visual detection, and the method comprises the following steps: firstly, recording brightness changes of pixel points in different illumination directions by constructing an illumination response matrix, and establishing a complete mapping of surface reflection characteristics; secondly, performing joint calculation by using the light source direction matrix and the illumination response matrix to obtain normal vector data and reflectivity data, and representing spatial geometric features and material optical features of the surface respectively; thirdly, calculating a gradient feature map, a curvature feature map and a deformation feature map based on the normal vector data, generating a reflectivity map by using the reflectivity data, and forming a multi-dimensional feature representation system; and finally, in a defect detection stage, a strategy of combining gradient feature pre-screening with multi-feature verification is adopted, and final judgment is carried out by calculating a comprehensive defect feature value, so that the technical problem of insufficient micro defect detection precision of a visual detection method in the prior art is solved.

The invention discloses a three-dimensional visual detection method, system and equipment for industrial application, and relates to the related field of three-dimensional computer vision, and the method comprises the steps: obtaining a three-dimensional point cloud data set of a target product, carrying out the multi-dimensional feature analysis based on the three-dimensional point cloud data set, and constructing a multi-modal feature vector set; performing three-dimensional visual defect detection according to the multi-modal feature vector group to generate a defect detection quality report; and making a control decision instruction according to the defect detection quality report, and automatically controlling the target product through the control decision instruction. The technical problem that existing industrial application-oriented three-dimensional visual detection is insufficient in detection accuracy and comprehensiveness is solved, and the technical effect of improving the detection accuracy and comprehensiveness is achieved.

The invention discloses a printed circuit board withstand voltage detection and evaluation method, which comprises the following steps: in the whole withstand voltage detection process, aiming at different process batches and environments, acquiring electrical signals, process parameters, environment variables and multi-modal visual data, and carrying out time sequence synchronization marking and standardized preprocessing; extracting multi-source features by using a feature coding algorithm, and establishing a multi-mode distribution model covering signal mutation, visual defects and working condition anomalies; through cross-modal feature fusion and a time sequence causal inference model, an abnormal event causal relationship graph is automatically constructed, and a voltage withstanding failure root cause chain is identified and positioned; according to the method, the efficient structured management of the detection data, the accurate traceability of the abnormal reasons and the cross-batch analysis are realized, and the capability of diagnosing and preventing the withstand voltage abnormality of the printed circuit board is remarkably improved.

The invention discloses a few-sample visual defect detection method based on approximate proximity search, and belongs to the technical field of industrial intellectualization. The system mainly comprises an image acquisition module, a feature extraction module, a memory bank construction module, a feature compression module, an approximate nearest neighbor search module and an anomaly detection and result output module. The feature extraction module is used for performing feature extraction on the collected image; the feature compression module is used for reducing the feature data volume and reducing the internal memory and calculation burden; the memory bank construction module is mainly operated during system initialization or model training and is used for constructing a feature memory bank of normal samples; the approximate nearest neighbor search module is responsible for quickly finding normal sample features closest to the input features in the memory bank; and the anomaly detection and result output module is used for converting distance information obtained by neighbor search into anomaly. According to the few-sample visual defect detection method based on the approximate proximity search, the effect of few-sample anomaly detection is achieved.

The invention provides a roadway surface and internal defect synchronous sensing method based on point cloud registration. The roadway surface and internal defect synchronous sensing method comprises the following steps: S1, multi-sensor joint calibration; s2, synchronously acquiring data; s3, data preprocessing; s4, projecting a camera image to the point cloud; s5, surface defect identification: S5.1, geometric defect identification based on point cloud; s5.2, performing visual defect identification based on the image, and marking an identification result in the point cloud; s6, integrating the geometric defect point cloud and the visual defect point cloud; s7, identifying internal defects; s8, determining an internal confirmation three-dimensional position; s9, marking an internal defect point cloud; and S10, uniformly visualizing the surface defects and the internal defects. According to the invention, through cooperative work of the laser radar, the ground penetrating radar and the camera, the same perception of tunnel defect surface and internal defects is realized through point cloud visualization.

The invention provides an energy storage device damage diagnosis and interaction system based on penetration vision and a large model, and relates to the technical field of energy storage device damage diagnos.The energy storage device damage diagnosis and interaction system comprises a penetration type 3D sensing module, a periodic topology analysis module, a feature mapping and retrieval module and a large model reasoning and interaction module, and the energy storage device is scanned and reconstructed; the method comprises the following steps: constructing a time-space decoupling periodic Transform network, introducing a periodic mask matrix to force the network to pay attention to a repeatability rule of an internal structure of a battery, and identifying internal tiny deformation and structural damage by calculating topological consistency under the condition that a large amount of negative sample training is not needed; visual defect features are mapped into text embedding by utilizing a feature projection technology, a diagnostic report containing physical cause analysis and maintenance suggestions is generated by combining a retrieval enhancement generation technology and a large language model, and a user is supported to perform interactive questions and answers in a natural language. The method can solve the problems that in the prior art, three-dimensional deformation is difficult to quantify, small samples are difficult to train, and intelligent decision-making ability is lacked.

The invention relates to a training method of a defect detection model and a PCB defect detection method, and the training method comprises the steps: carrying out the feature extraction based on a first training image through a first network branch of an initial detection model, and obtaining a visual defect feature map; performing feature extraction based on a second training image through a second network branch of the initial detection model to obtain a thermodynamic defect feature map; fusing the visual defect feature map and the thermal defect feature map by using the initial detection model to obtain a composite defect feature map, and performing detection based on the composite defect feature map to obtain a prediction result; and constructing a physical constraint item according to the space gradient information of the thermodynamic defect feature map, constructing a joint loss function according to the defect label, the prediction result and the physical constraint item, and performing iterative training on the initial detection model based on the joint loss function to generate a defect detection model. According to the invention, the problem of low accuracy of PCB defect detection is solved.

The invention discloses a multi-station synchronous plastic cup spoon full-surface visual defect detection device and method, and relates to the technical field of visual detection.The multi-station synchronous plastic cup spoon full-surface visual defect detection device comprises a multi-station rotating platform, multiple sets of flexible jigs used for clamping parts to be detected, a multi-station detection module and a detection controller; the four detection modules sequentially cover surface texture, three-dimensional morphology, material characteristics and mechanical property full-dimensional defects of a to-be-detected part; 2D characteristics such as surface smudginess, scratches and printing defects are captured through a multispectral camera; the 3D point cloud reconstruction module identifies three-dimensional shape defects such as pits and deformation through laser or structured light scanning; the hyperspectral material detection module is used for identifying material variation and wear degree through spectral analysis; the dynamic stress test module detects hidden mechanical defects such as stress whitening and microcracks through a loading test.

Users exploiting near-to-eye (NR2I) displays for augmented reality and / or correction of low vision are typically wearing these NR2I displays for specific tasks or for extended periods of time, potentially all their time awake. Conflicting tradeoffs between user comfort and minimal fatigue and strain during use, ease of attachment, minimizing intrusiveness and aesthetics should be concurrently balanced with providing an optical vision system that provides the user with a wide field of view, high image resolution, and a large exit pupil for eye placement with sufficient eye clearance. Accordingly, embodiments of the invention provide NR2I displays meeting these conflicts whilst also addressing the inherent variations between user in respect of their head dimensions, head and eye geometry, as well as adapting the displayed content to reflect the user's task at-hand, visual defects or degradations, visual focus and intent upon various regions-of-interest within their field of view. Such HMDs providing enhanced user experiences.

The invention discloses a DETR-based concrete pouring vibration apparent quality evaluation method and system. The method comprises the following steps: acquiring a concrete surface image; performing semantic segmentation and defect detection on images in the data set by adopting a concrete pouring vibration appearance detection model; and performing quantitative scoring on the concrete pouring vibration apparent quality according to a defect detection result. By fusing a deep learning technology and a visual detection means, high-precision automatic identification and quality evaluation of concrete surface defects (such as cracks, honeycombs and holes) are realized, the problems that traditional concrete vibration apparent quality evaluation depends on artificial experience, the efficiency is low and the subjectivity is high can be solved, and the quality evaluation accuracy is improved. The method is suitable for visual defect detection and quality grading based on deep learning.

The invention relates to the technical field of image acquisition, in particular to a non-contact visual defect acquisition system and defect detection method for disc scissors, comprising an image acquisition device and a control device electrically connected with the image acquisition device; the image acquisition device comprises a camera and an LED stroboscopic lamp, and by combining the stepping distance between acquisition frames and the acquisition position of the camera with the rotation speed of the disc shear tool, the image acquisition rhythm matched with the high-speed rotation of the disc shear tool can be realized, the detailed information of each part of the disc shear tool can be quickly acquired, and the image acquisition efficiency is improved; through matching of the PWM stroboscopic control frequency of the LED stroboscopic lamp and the exposure time of the camera, it is ensured that stable and appropriate illumination intensity and illumination time are provided when the camera is exposed every time, the problems of image blurring, inconsistent brightness and the like caused by uneven illumination or illumination change are solved, and the system is simple in structure, convenient to operate and low in cost. The problems that a traditional disc type cutter is low in detection efficiency, large in error and lagged in detection are effectively solved.

The present application belongs to the field of machine vision defect detection, and in particular to a thermocell electrode sheet vision defect detection method and a vision defect detection device. Product size information of a thermocell electrode sheet is obtained, and the field of view of a camera is configured. A thermocell electrode sheet to be detected is placed on a detection platform, front surface images, side surface images, back surface images, and inner hole images of the thermocell electrode sheet are captured by multiple direction detection cameras, and are processed to obtain the to-be-detected images of the front surface images, the side surface images, the back surface images, and the inner hole images. According to the to-be-detected images of the front surface images, the side surface images, the back surface images, and the inner hole images, a trained YOLO8 recognition model is used to describe and determine defects.

The invention discloses an AI visual defect identification and detection method integrated with a light supplementing module, and relates to the technical field of computer vision, and the method comprises the following steps: S1, data acquisition: carrying out multi-angle shooting on a product at a fixed station of a production line by using an industrial camera integrated with the light supplementing module, the light supplementing module automatically adjusts the illumination intensity and angle according to product surface characteristics and ambient light conditions; s2, image preprocessing; s3, feature extraction; s4, defect classification / positioning; and S5, outputting a result. According to the AI visual defect identification and detection method integrated with the light supplementing module, the light supplementing module is integrated, and the programmable LED light source array is adopted, so that the change of the illumination intensity of a production line can be sensed in real time, and the illumination intensity and the angle can be automatically adjusted, and therefore, the problem that the local part of an image is too bright or too dark due to ambient light fluctuation can be effectively avoided; especially for highly reflective materials such as metal and coated surfaces, the light supplementing module can select the angle combination with the weakest reflected light for light supplementing.

The invention relates to the technical field of bridge detection, in particular to an unmanned aerial vehicle monitoring method for quality inspection of large-span bridge pier studs, comprising the following steps: S1, configuring a multi-modal sensor on an unmanned aerial vehicle to obtain pier stud layout information, and planning an unmanned aerial vehicle route based on the pier stud layout information; s2, automatically executing a flight task according to the route of the unmanned aerial vehicle, and collecting a multi-modal image and spatial position information; when the device is used, the visual, depth and thermal information is fused, so that the device is beneficial to more comprehensively and accurately identifying the structural abnormality and thermal damage of the pier stud, improving the image acquisition precision, constructing a three-dimensional model of structural defects, accurately measuring the length, width, depth and other size information of cracks, and binding the identification result with an actual structural model; and generating a three-dimensional visual defect marking graph and a statistical report, and finally outputting a visual defect result, thereby being beneficial to identifying the damage type of the pier stud and improving the precision of an identification mode.

The invention relates to the technical field of computer vision, and discloses an industrial product visual defect detection method and system based on multi-task reverse knowledge distillation, by introducing a multi-scale projection layer and a multi-task learning framework, the expression ability of a model to normal sample features and the inhibition ability to abnormal signals are significantly improved, and the detection accuracy is improved. Therefore, industry-leading defect detection and positioning precision is realized on industrial reference data sets such as MVTec, extremely low reasoning delay and memory occupancy are kept, the reasoning speed is far higher than that of mainstream methods such as PatchCore, an efficient and accurate technical scheme is provided for industrial large-scale real-time defect detection, low delay and low memory consumption are kept, and the defect detection accuracy is greatly improved. The abnormal detection and positioning precision of industrial product visual defect detection is remarkably improved, and the defect that performance and efficiency are difficult to consider in the prior art is overcome.

The invention relates to the technical field of visual inspection, and provides a lens flaw detection method based on deep learning and related equipment thereof. Visual defect feature extraction is performed on an original lens image to obtain a defect candidate region set, multiple feature discrimination is performed on the defect candidate region set in combination with a visual algorithm to obtain an initial defect region set, and an area threshold and the initial defect region set are compared and screened to obtain a to-be-detected region set. And performing forward reasoning on the to-be-detected region set through a ShuffleNetV2 depth model to obtain a defect type and a confidence value of each region, performing defect judgment on the defect type of each region according to a confidence threshold and the confidence value to obtain lens defect data, and performing defect elimination according to a defect elimination mode and the lens defect data. Through visual preprocessing, multi-feature discrimination, region screening and lightweight deep model reasoning, high-precision, low-delay, easy-to-deploy and high-robustness lens flaw detection is realized.

The invention discloses a visual defect detection method, device and equipment based on knowledge prompt and a storage medium, and relates to the technical field of appearance defect detection.The method comprises the steps that multi-dimensional image data corresponding to a to-be-detected object is collected, and the multi-dimensional image data comprises a multi-band image, a multi-polarization image and an active structure light phase image; generating knowledge prompt information of the to-be-detected object according to a preset knowledge graph; and performing fusion defect detection based on the multi-dimensional image data and the knowledge prompt information to obtain a defect detection result. According to the method, the comprehensive information of the to-be-detected object is obtained from different dimensions by collecting the multi-dimensional image data, semantic guidance is provided by combining the knowledge prompt information generated by the knowledge graph, cross-modal fusion defect detection of the to-be-detected object is achieved, and therefore the visual defect detection precision is improved.

The invention discloses a substation equipment defect detection method and device, and the method comprises the steps: constructing an improved Yolov9 model, reconstructing a convolution improved RepNCSPELAN4 module through a space and a channel, introducing a convolution and attention mixing module into the model, and improving the detection effect of a small target. Before feature prediction, a depth separable convolutional layer is introduced to enhance feature representation; aiming at the poor detection effect of small objects in the image, fine tuning training is carried out by using slice assistance, an original image is segmented into a plurality of overlapped slices to enhance a data set, and the detection capability of a model on small target defect types is improved; and the trained model is used for visual defect detection of the appearance of the power equipment, and when the equipment is detected to have defects, an alarm is immediately sent to a platform. The method can meet the basic requirements of substation equipment defect detection, can help the substation to carry out equipment monitoring and protection, reduces the potential safety hazard, and improves the stability of equipment operation.

This invention discloses a machine vision-based method and system for identifying defects in wire ropes of port cranes, belonging to the field of visual defect recognition technology. The method includes: real-time acquisition of wire rope images; real-time acquisition of the wire rope's motion state data, including acceleration and angular velocity, via an inertial measurement unit; prediction of the wire rope's current motion state using a Kalman filter algorithm based on the previously acquired motion state data; construction of a fuzzy kernel function based on the predicted current motion state, and sharpening of the currently acquired wire rope image using an inverse filtering algorithm; and defect identification using a target detection model on the sharpened image. This invention enables rapid and accurate detection of surface defects in wire ropes under dynamic fuzzy and complex environments.

The invention discloses a visual defect identification and evaluation method for aviation I-shaped edge strip processing quality, particularly relates to the field of machine vision detection and discrimination, and is used for solving the problem of mistaken elimination or length measurement distortion caused by easy confusion of end part short region slender defects and end surface mirror reflection secondary images. The method comprises the following steps: establishing part local coordinates and fillet belt unfolding coordinates; screening a main evaluation view angle and a dual view angle and generating a displacement prediction window; extracting a candidate slender response set through dual-view collection and local registration; calculating a mapping residual distance and a same shift segment length and generating a mirror pseudo coefficient; and executing gating judgment and mirror image mask removal or length measurement and category judgment, and binding and storing to finish evaluation.

The invention relates to the technical field of visual defect detection, in particular to an intelligent dam surface defect detection method based on computer vision, which comprises the following steps: S1, acquiring a dam surface image sequence to form an original detection image set; s2, performing adaptive light field compensation and noise suppression processing on the original detection image set; s3, multi-scale region segmentation is carried out, curvature features are extracted, and a structural feature map is generated; s4, executing three-dimensional texture reconstruction on the basis of the structural feature map, and extracting a surface topography feature vector; s5, defect feature coupling analysis is carried out, and a defect classification matrix is generated; and S6, performing coordinate mapping in combination with a geographic information system, and outputting a defect detection report containing space coordinate information. According to the method, through fusion of multispectral imaging, three-dimensional texture reconstruction and geographic coordinate mapping technologies, high-precision identification and spatial positioning of dam surface defects are realized, and the detection accuracy and the operation and maintenance response efficiency are remarkably improved.

The utility model relates to a cable on-line size and surface defect detection device, which belongs to the technical field of cable production and comprises a fixing frame, an optical size measuring assembly, a visual defect detection assembly and a first light source. The optical dimension measuring assembly and the visual defect detecting assembly are fixed to the two sides of the fixing frame respectively, the first light source is fixed to the fixing frame, and the purpose is to improve the accuracy of cable detection.

The application belongs to the technical field of optical lens detection, and particularly relates to an optical lens appearance defect detection device, which comprises a supporting assembly, a feeding device is installed on the supporting assembly, a first discharging slide plate is installed at the discharging end of the feeding device, a mounting seat is fixedly connected to the supporting assembly below the first discharging slide plate, supporting rollers are rotationally connected to both ends of the mounting seat, and the cooperation of the structures such as the belt ring, the first visual defect detection camera, the pushing cylinder, the guide plate and the driving roller is achieved, so that after the optical lens is accurately positioned in the positioning groove formed in the belt ring, the first visual defect detection camera arranged on both sides is used to detect the end of the optical lens, then the pushing cylinder is used to push the optical lens to the driving roller, the driving roller is rotated to drive the optical lens to slowly rotate, the second visual defect detection camera is used to detect the circumferential side of the optical lens, and the mechanical hand is not needed to be used for carrying and aligning, so that the detection efficiency is greatly improved.

The application relates to the technical field of machine vision and image processing, and discloses a plastic valve visual defect real-time detection method and system based on deep learning, which comprises the following steps: acquiring a gray image containing a plastic valve, positioning the centroid of a plastic valve main body area by using geometric moments; converting the gray image to a polar coordinate based on the centroid, applying one-dimensional high-pass filtering, and determining a dynamic segmentation threshold to extract abnormal gray pixel points; performing spatial density clustering after reverse mapping to determine a suspected defect area; calculating the Euclidean distance between the centroid of the suspected defect area and the centroid of the plastic valve main body area, synchronously inputting the distance and image features into a double-input convolutional neural network to output a plastic valve defect detection result. The application filters out cloud and fog-shaped diffuse reflection interference, and improves the accuracy of defect detection.

The invention relates to a visual aid (1) comprising at least one imaging optic (2) with variable focal length for positioning between a viewer's eye and a scene to be viewed, further comprising means for controlling the change in focal length of the at least one imaging optic, optoelectronic means (3) for determining the distance of the imaging optic (2) optionally from one or more nearest objects in the scene to be viewed in the viewing direction of the viewer's eye and / or with a selectable angular tolerance around the viewing direction of the viewer's eye, electronic means for controlling the means for controlling the change in focal length of the at least one imaging optic (2), wherein said determined distance is converted into a target focal length of the at least one imaging optic (2) by means of a table of values ​​and / or by means of an equation.as well as an energy source for the aforementioned optoelectronic and electronic means and the means for control. The invention further relates to a method for vision correction and a computer program.

The present invention is to introduce a computer-implemented, web-based stereoscopic acuity computation and stereo eye color accuracy assessment to test for what colors patients are deficient in as seen through the real world in pictures rather than just for color by itself. The pictures are taken with a camera or similar electronic device use. The eye color accuracy exam utilizes caffeine products. The computation takes color characteristics of a target color where they are inputted, identified matched, and processed in a way as to expose a visual deficiency of the target color selected from a color database and color chart based upon color characteristics of the target color to be matched, processed, and visually stereoscopically displayed thus enabling comparison of the alternate color(s) with the target color. The computation uses gamma correction, color thresholding, alpha composing, opacity ratios and eye color accuracy assessment from a patient to provide a result.

The utility model relates to the technical field of visual defect detection, in particular to a workpiece AI visual defect detection mechanism which comprises a U-shaped bottom plate, the inner side face of the U-shaped bottom plate is rotationally connected with a threaded rotating rod, the outer surface of the threaded rotating rod is in threaded connection with a connecting block, and a first electric push rod is installed on the lower side of the connecting block. The visual defect detection mechanism can be adjusted and moved back and forth through the connecting block so as to adjust the detection position of the AI visual camera below, so that the AI visual camera can adapt to workpieces at different positions, the flexibility of the visual defect detection mechanism is ensured, and the detection angle of the AI visual camera can be adjusted along with the rotation of the movable rotating plate; according to the AI visual defect detection mechanism, the side face of the workpiece is conveniently and comprehensively detected, so that the detection precision and effect of the AI visual defect detection mechanism are improved, the production quality is guaranteed, the whole AI visual camera is conveniently assembled in a clamping mode, the AI visual camera is conveniently assembled and disassembled, and the AI visual defect detection mechanism is more convenient to use.

The invention relates to the field of wafer machine vision defect classification, and discloses a wafer machine vision defect classification device which comprises a bottom plate, a supporting column is arranged in the middle of the upper end of the bottom plate, a telescopic rod is arranged at the upper end of the supporting column, and a first rotating rod is connected to the upper end of the telescopic rod. A first inner rotating shaft is connected to the overlapping position of the first rotating rod and the telescopic rod, a second rotating rod is arranged at the upper end of the first rotating rod, a second inner rotating shaft is connected to the overlapping position of the second rotating rod and the first rotating rod, and a third rotating rod is arranged at the upper end of the second rotating rod. The wafer center is accurately positioned, it is ensured that the geometric center of the wafer is aligned with a visual system during detection, the position error is eliminated, the defect classification precision is improved, the supporting round rod serves as a transfer platform, a temporary placement point is provided for the wafer, the wafer center is preliminarily centered, and the subsequent positioning step is simplified; and accurate positioning of the circle center of the wafer is realized through flexible pressing.

The application discloses a kind of based on improved AnoGAN small sample substation equipment visual defect detection method, belong to machine learning technical field;The method comprises: obtaining training substation equipment image;According to normal substation equipment image training AnoGAN adversarial generation network;Defect substation equipment image is input into AnoGAN adversarial generation network after training is completed;After pre-processing, defect substation equipment image is input into pre-training similarity classification network and is classified;After pre-processing, defect substation equipment image is input into pre-training convolution classification network and is classified;According to similarity classification result and convolution classification result, obtain defect detection result.The application also provides a kind of based on improved AnoGAN small sample substation equipment visual defect detection system.The network of the application is trained to normal data for training, so its generation effect can be distinguished to all non-normal state equipment, and the practical application is more widely used.

The present invention relates to an aerosol-generating article (1) comprising a filter (2) and an aerosol-generating rod (6). The filter includes at least one filter segment. The filter segment includes a filter media comprising a liquid additive and at least one filter wrap. The filter wrap may be, for example, a filter segment package. A filter wrap extends from a first end of the filter to a second end of the filter to wrap the filter media.