474 results about "Defect region" patented technology

The invention provides a defect detection method and system for integrated circuit manufacturing. Wherein in the integrated circuit packaging stage, packaged chips are divided into a first type of chips meeting a preset physical characteristic threshold value and a second type of chips exceeding the physical characteristic threshold value through a pneumatic sorting device; performing scanning electron microscope imaging on the surface of the second type of chip, generating a three-dimensional topological structure by using an image reconstruction technology, and positioning a micron-sized particle defect area by comparing reference type differences; synchronously applying high-frequency electric signal excitation in the sorting process, detecting impedance response of the second type of chips, and recording an abnormal signal sequence exceeding a preset frequency domain range; performing space-time correlation matching on the micron-sized particle defect area and the abnormal signal sequence, and judging that the defect is a multi-layer composite defect when a preset coupling condition is met; and dynamically adjusting the mesh diameter and vibration frequency parameters of the pneumatic sorting device. According to the technical scheme provided by the invention, accurate identification of the multi-layer composite defect of the chip is realized, and the defect interception efficiency is remarkably improved.

The invention provides an industrial part defect detection method, which comprises the following steps: acquiring an input industrial part burr defect image, extracting the shape, size and distribution position characteristics of burrs in the image to obtain a burr characteristic pattern, segmenting a burr defect region according to the edge roughness and color difference in the burr characteristic pattern, and generating a binary burr defect mask; performing morphological operation on the binarized burr defect mask, eliminating isolated noisy points through expansion and corrosion, connecting adjacent burr areas, segmenting the processed burr mask, and extracting independent burr defect areas according to the three-dimensional contour and surface texture features of burrs; performing image enhancement processing on the preliminary verification code character matrix, adjusting the color contrast of characters to highlight the thickness of character strokes, and generating a verification code image; and carrying out compression and format conversion on the generated verification code image to obtain a final industrial part burr defect verification code which is used for defect detection of workpiece parts.

Disclosed in the embodiments of the present application are a defect detection method and apparatus, an electronic device, a storage medium and a program product. The method comprises: acquiring an image under test of a target workpiece and a template image of the target workpiece, and comparing the image under test with the template image so as to obtain a differential image; inputting the image under test and the differential image into a defect detection model, and executing the following processing: on the basis of the image under test and the differential image, extracting a target image feature and, on the basis of the target image feature, performing defect detection on the image under test to obtain defect detection box information of the image under test; positioning a defect area in the differential image and, according to the defect area, determining a first detection result of the target workpiece; and, according to the defect detection box information and the first detection result, determining a second detection result of the target workpiece.

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention provides a regeneration wafer defect detection method and system based on point cloud, and belongs to the technical field of semiconductor manufacturing detection, and the method comprises the steps: carrying out the registration and fusion processing of the point cloud data of a target wafer, and obtaining the target point cloud data; performing plane fitting on the target point cloud data to determine a reference plane; performing feature extraction on the target point cloud data relative to the reference plane to obtain feature data; positioning a defect area based on the feature data, and calculating geometric parameters of the defect area; correcting the geometric parameters by using a preset error compensation model to obtain target geometric parameters; inputting the position of the defect area, the target geometric parameter and the feature data of the corresponding defect area into a defect classification model to obtain a defect type of the defect area; and generating a defect detection result of the target wafer based on the defect types, the positions, the target geometric parameters and the feature data of all the defect areas of the target wafer. According to the invention, the efficiency and reliability of regenerated wafer defect detection are improved.

The invention relates to the technical field of PCB detection, and discloses an online defect detection method and system in the PCB production process and computer equipment, the detection accuracy can be improved by combining production parameters, image information and a design circuit diagram, potential defects can be found in the early stage, the PCB is divided into a plurality of areas, and the detection efficiency is improved. According to the PCB, each area can be independently welded and detected, the complexity of the whole PCB is reduced, the PCB is divided into a plurality of areas which are detected one by one, a specific defect area can be effectively positioned, through the regionalized detection and maintenance process, the unqualified area can be locally processed, the production efficiency is improved, and the production cost is reduced. By adopting the strategy based on multi-round detection, feedback and regional repair, through accurate positioning, local repair, detection efficiency improvement and automatic processing, the rejection rate of unqualified products is greatly reduced, the maintenance success rate is improved, and the problems of difficulty in PCB defect detection and resource waste in the prior art can be effectively solved.

The invention discloses a defect detection method and device based on phase deflection, and a storage medium, which are used for improving the detection capability of different significant defects. Obtaining a reflection fringe image of the display screen to be detected; performing phase extraction and phase unwrapping processing on the reflection fringe image to generate phase distribution data; extracting gradient data of the phase distribution data; according to the gradient data and a saliency threshold value, multi-level saliency recognition is carried out on the defect area of the reflection fringe image, multi-level saliency recognition results are generated, and the multi-level saliency recognition results comprise a high saliency detection result, a middle saliency detection result and a low saliency detection result. Different levels of saliency recognition results correspond to different types of defects; performing hierarchical classification processing on the multi-level saliency recognition result; and outputting a final detection result after grading and classification processing.

The invention relates to the technical field of image processing, in particular to a training method and system based on a circuit board defect detection model. The method comprises the following steps: acquiring an automatic optical monitoring image of the circuit board, and carrying out circuit board surface defect detection on the automatic optical monitoring image of the circuit board so as to obtain surface defect data of the circuit board; marking a circuit board defect area according to the circuit board surface defect data so as to obtain circuit board defect area data; carrying out welding spot three-dimensional contour imaging according to the automatic optical monitoring image of the circuit board so as to obtain welding spot three-dimensional contour data, and carrying out circuit board pseudo soldering analysis according to the welding spot three-dimensional contour data so as to obtain circuit board pseudo soldering data; and performing electrical connection defect analysis according to the circuit board defect area data so as to obtain electrical connection defect data. Based on the computer vision technology, the accuracy of the circuit board defect detection model is improved.

The invention discloses a method and device for filtering scratch defects of a display screen and a storage medium. The method and device are used for reducing the situation that the defects of the display screen are over-detected. Collecting a scratch filtering auxiliary image and a to-be-filtered image; determining a scratch defect area on the scratch filtering auxiliary image; determining a line defect area on the image to be filtered; determining diagonal coordinate information of minimum envelope areas of all defect areas; selecting a to-be-detected defect area, and filling the to-be-filled image with the row and column coordinate information of the remaining first defect area; screening the area of the to-be-filled image to generate a card control area; determining a second defect area meeting a basic adjacency requirement; determining a matched defect area of the defect area to be detected; and repeating the above steps on the matched defect area and the remaining area to obtain an integrated defect area, removing the integrated defect area from the first defect area, and re-selecting a new area to be detected to calculate the matched defect area until no defect area exists in the first defect area.

The invention discloses a molybdenum-rhenium alloy pipe fitting defect identification method and system based on a high-definition microscopic image. The method comprises the following operation steps that high-definition microscopic equipment is used for collecting the high-definition image of a molybdenum-rhenium alloy pipe fitting; preprocessing the high-definition image of the molybdenum-rhenium alloy pipe fitting to obtain a molybdenum-rhenium image to be detected; performing execution processing of a region growing algorithm on the molybdenum-rhenium image to be detected to obtain a defect region to be detected; performing defect identification on the to-be-detected defect area to obtain type defects; and judging the defect problem of the molybdenum-rhenium alloy pipe fitting according to the type defect.

The invention provides a wafer defect detection method and system and electronic equipment, and relates to the technical field of machine vision, and the method fully utilizes the transformation relation between an imaging unit and an objective table in the wafer defect detection process to precisely splice strip images, thereby obtaining a high-precision wafer image, and improving the wafer detection precision. Meanwhile, an improved VAE model can be adopted to accurately obtain the defect area of the wafer to be detected from the brightness, the contrast ratio and the structural difference, high-precision recognition can be carried out on the fine defects, and therefore the problem that in the prior art, the fine defect detection effect is poor is solved.

The invention provides a semiconductor chip defect detection positioning method and system, and relates to the technical field of semiconductor chip detection, and the method comprises the steps: obtaining an original image and electrical test data; forming a defect candidate area based on local density analysis; variational mode decomposition is carried out on the electrical data, and electrical characteristic abnormal points are marked; mapping the image features to an electrical characteristic space and calculating mutual information values to determine defect points; and calculating the distribution and path of a diffusion field and determining the severity of the defect. According to the invention, accurate positioning and severity evaluation of chip defects are realized, and the detection efficiency and accuracy are improved.

The invention belongs to the technical field of image data processing, and particularly relates to a quality detection method and system for metal pipe fitting forming, and the method comprises the steps: obtaining a plurality of groups of matched gap regions of a port edge image according to the gray scale difference of the neighborhood of each gap of the port edge image in a corresponding metal pipe fitting image; obtaining a suspected defect area of the port edge image according to curvature change of edge pixel points of the port edge image; acquiring similar arc segments of each suspected defect area according to the change of the distance from the arc segment on the ellipse of the metal pipe edge image to the ellipse center; obtaining the abnormal degree of each suspected defect region according to the deformation difference between the suspected defect region and the similar arc section and the gray difference between the suspected defect region and the corresponding suspected defect region of the same group of matched gap regions; and screening to obtain a final defect area. According to the invention, the accuracy of quality forming detection of the metal pipe fitting is improved.

The invention discloses a fabric defect detection method based on image reconstruction, which is characterized in that a U-shaped self-coding model with a space self-attention module, a channel self-attention module and jump connection is used, and only a defect-free normal image is used during model training; according to the method, a diffusion process of gradually adding noise and an anti-diffusion process of gradually removing noise are carried out, so that the model learns to gradually remove mixed noise in an image, a defect image with noise is reconstructed into a noiseless and defect-free image by the model in a test stage, and the steps of acceleration, visualization of a defect area and the like are realized by using a step length resetting method. Compared with the prior art, the method provided by the invention has the advantages that model training can be carried out only by using a normal image, the detection effect can be improved, a defect area with pixel-level fineness can be output, meanwhile, the denoising frequency during detection is reduced, the real-time performance of defect detection is improved, the problem of too long reconstruction time caused by repeated denoising of the model is relieved, and the reconstruction efficiency is improved. Good application prospects are realized.

The invention relates to the technical field of defect detection, in particular to a die-casting die defect detection method and system based on image recognition, and the method comprises the following steps: irradiating the surface of a die-casting die by using a target wavelength light source, capturing a die surface image by using a camera, and carrying out the color correction and illumination compensation of the captured die surface image, and storing the output high-definition original image. According to the method, the color depth and the contrast ratio of the high-definition original image are adjusted, so that the highlighted display of defect features is greatly improved, the recognizability of surface defects such as cracks and air holes is enhanced, the detection accuracy is improved, detail revelation and defect contour recognition are carried out, and the separation of a defect area and a background is further accurate; the accuracy and efficiency of analysis are optimized, accurate quantification of the shape and size of the defect is achieved through gray level analysis, quantitative data are provided for defect management and die maintenance, the service life of the die-casting die is comprehensively prolonged, and the production problem caused by the defect is relieved.

A method of repairing a defect on a surface is presented that includes imaging the surface to locate the defect with a first imaging system. The method also includes conducting a repair operation by contacting the surface with an abrasive article. The abrasive article is pressed into contact with the surface in an area of the defect by a robotic repair system. The method also includes imaging the abraded surface, with a second imaging system. Imaging includes scanning the surface in the defect area to obtain a topography of the defect area, passing the second imaging system over the defect area such that a distance between the second imaging system and the surface is maintained. Imaging also includes generating an image of the defect area, wherein the image is a near dark field image or a dark field image and generating an evaluating regarding the repair operation based on the generated image.

The invention relates to the technical field of defect detection, in particular to a defect detection method, system and equipment based on improved YOLOv5 and a medium. Firstly, image data of industrial parts in the production process are collected through a high-definition camera, image preprocessing is carried out according to part surface defect characteristics, key frames are extracted, defect areas are marked, and a defect detection data set is constructed; secondly, configuring a training environment; constructing a CED-YOLO network model by taking the YOLOv5 network as a basic network, replacing a C3 module in a backbone network of the YOLOv5 network with a CEMS module as a feature extraction module, replacing a C3 module of a neck network connection detection layer part with differential attention DA, and enhancing extracted features; and finally, the CED-YOLO network model is trained and verified by using the data set after data enhancement, so that the calculation amount is effectively reduced, the detection efficiency is improved, and the detection performance of the model is greatly improved.

The invention discloses a chip surface defect detection method, electronic equipment and a readable medium. The method comprises the following steps: aligning an ROI (Region of Interest) of a reference image with a test image through affine transformation; obtaining a gray average value of the ROI of the reference image and the ROI of the aligned test image, and carrying out gray stretching normalization processing on the test image based on the gray average value; respectively carrying out mean filtering on the reference image and the test image after normalization processing, and carrying out differential operation to obtain a differential image; performing threshold-based binary segmentation on the difference image, extracting surface detection candidate areas of bright defects and dark defects, performing morphological corrosion operation on the surface detection candidate areas to suppress noise, and generating a final defect mask; and mapping a defect area in the final defect mask back to the original test image coordinate system through inverse transformation of affine transformation. According to the method, high-precision smudginess defect extraction of the IPM chip area under the background of complex brightness can be realized, and the method is suitable for various complex packaging working conditions.

The invention provides a defect type detection method and device, electronic equipment and a storage medium. The detection method comprises the following steps: determining a plurality of first defect areas and corresponding first defect types in a first image of a target object acquired by first image acquisition equipment, and screening out defect parts to be detected from the plurality of first defect areas; second image acquisition equipment is used for acquiring a to-be-detected image comprising the defect part of the target object, and the image quality of the first image is different from that of the to-be-detected image; aiming at the to-be-detected image, determining a defect area corresponding to the defect part; for the to-be-detected image, determining a defect region feature of the defect region, the dimension size of the defect region feature on each dimension being a preset value irrelevant to the area of the defect region; and determining the defect type of the target object in the defect part based on the defect area features. By adopting the method and the device, the calculation amount of secondary reinspection can be reduced, and the method and the device can be better focused on concerned defect types.

The invention relates to a defect detection method and device for an optical element, computer equipment and a storage medium. The method comprises the following steps: acquiring a target image of an optical element; based on the trained defect detection model, performing defect detection on the target image to obtain a defect identification frame corresponding to at least one detection target and a corresponding defect type label; the defect detection model is obtained based on deep learning model training; and carrying out fusion processing on the defect identification frames of the labels with the same defect type to obtain a defect area in the optical element. By adopting the method, adjacent or overlapped same-class defect areas can be effectively merged, so that a complete defect contour and a complete distribution range can be accurately restored.

The invention discloses a defect detection method and system for integrated circuit packaging, and the method comprises the steps: obtaining a scanning image of an integrated circuit packaging sample, and analyzing the scanning image to determine a defect region of the integrated circuit packaging sample; multi-modal data of the defect area are collected and fused, and comprehensive defect information of the integrated circuit packaging sample is obtained; counting defect types of the integrated circuit packaging sample and performance influence parameters caused by various types of defects according to the comprehensive defect information; and screening out to-be-repaired defects and negligible defects according to the defect types and performance influence parameters caused by each type of defect, and generating a visual report. The comprehensive defect information is determined by positioning the defect area and detecting the multi-modal fusion data of the defect area, so that multiple types of defects of the integrated circuit packaging sample can be determined in an all-around manner, and the defect detection precision, reliability and detection efficiency are ensured.

The invention relates to a wafer defect detection method and system based on image neighborhood analysis, and the method comprises the steps: carrying out the image collection of a to-be-detected wafer, and obtaining a digital image containing a complete wafer back surface; performing traversal detection on each wafer back pixel point in the digital image; in the traversal detection process, through a dynamically adjusted single-size neighborhood window and / or a fixed multi-size neighborhood window, carrying out abnormity determination on the currently traversed wafer back pixel points until determination of all pixel points in the digital image is completed, and obtaining all abnormal pixel points; and carrying out clustering processing on the abnormal pixel points to obtain candidate defect areas in the digital image. Through the wafer defect detection method and device, a dynamic detection strategy and a fusion detection strategy are realized, the misjudgment risk of a global threshold is effectively overcome, the consistency of real defect detection is ensured, the defect detection precision of the wafer is improved, and the problem of how to improve the defect detection precision of the wafer is solved.

The invention belongs to the technical field of laser additive repair, and discloses a metal part surface defect automatic detection and laser additive repair method which comprises the following steps: (1) scanning the surface of a metal part by adopting a scanner, and preprocessing the obtained point cloud; (2) automatically detecting the defects of sinking, peeling and chipping on the surface of the metal part by analyzing the normal vector of each point in the point cloud, and automatically detecting the crack defect on the surface of the metal part by analyzing the density of the point cloud; (3) carrying out layering treatment on the point cloud corresponding to the sunken or stripped chipping defect, generating a laser scanning path in a sunken or stripped chipping defect area, and carrying out laser cleaning and laser additive repairing on the defect; and (4) according to the point cloud corresponding to the crack defect, a laser scanning path is generated in the crack defect area, and laser cleaning and laser additive repairing are conducted on the crack defect. According to the invention, the surface defects of the metal part can be efficiently detected and repaired, and the repair quality requirement is met.

The invention discloses a storage chip defect detection method and system based on deep learning, and relates to the technical field of defect detection.In the system operation process, interference behaviors in the access process are collected, chip dynamic interference indexes are established, the response behaviors are integrated in a multi-dimensional parameter form, and a map structure is generated; coupling feature representation suitable for deep model input is formed through fusion, weak image features, region abnormal structures or control response spots in the atlas are recognized, the position, type and confidence mark of a defect region are output, abnormal records are generated in a coding mode, the abnormal records of multiple time points are constructed into a time sequence, and the time sequence is stored in a database; and performing evolution trend modeling based on indexes of the abnormal activity, the position change trend and the activation frequency, outputting a trend function for health level evaluation, receiving a health level result, matching the health level result with a control strategy table, and executing chip protection, working state adjustment or fault reporting operation.

The invention provides an intelligent visual inspection method and system for hardware stamping die production, and relates to the field of hardware stamping die production, and the method comprises the steps: obtaining a die current state image of a to-be-detected hardware stamping die and a defect-free die state reference image, and carrying out the geometric alignment; and then calculating a difference graph to obtain significance distribution of a potential defect area, carrying out multi-scale difference feature hierarchical coding by using a pyramid network, and separating normal wear artifacts from real defects. Furthermore, a feature receptive field anchoring mechanism is introduced, and the attention weight is dynamically adjusted according to the geometric topology of the mold. And finally, a lightweight classifier is used to make a decision on the enhanced difference features, and sub-millimeter-level anomaly recognition is realized. In this way, the sensitivity to tiny changes of the hardware stamping die can be effectively improved, and defects can be found in the early stage.

The invention relates to the technical field of defect detection, in particular to a chip defect detection method and system based on a Gaussian attention mechanism, and the method comprises the steps: collecting a chip image, carrying out the preprocessing of the collected chip image, marking a defect region in the chip image, and generating a binary mask image reflecting the defect distribution; based on the binary mask image, combining distance transformation and local color change analysis to generate a Gaussian attention channel; connecting the Gaussian attention channel with the chip image in the channel dimension to generate an enhanced image; and executing a defect detection algorithm based on the enhanced image, and identifying a defect region in the chip image. According to the method, the sensitivity of the detection system to tiny and low-contrast defects can be improved, so that the false alarm rate is reduced, and meanwhile, the defect detection precision is effectively improved on the premise that the calculation complexity is not greatly increased.

The invention discloses a defect layout repairing method, device and equipment, a medium and a product, and relates to the technical field of semiconductor integrated circuits. The defect layout repairing method comprises the following steps: acquiring a first defect area and a second defect area in a mask design layout, wherein the first defect area comprises the second defect area; according to the first defect area and the second defect area, a first repair evaluation value of the first defect area and a second repair evaluation value of the second defect area are determined, and the first repair evaluation value is used for representing the graph offset degree of the first defect area caused by repair of the second defect area; the second repair evaluation value is used for representing the graph offset degree of the second defect area caused by repairing the second defect area; moving the second defect area according to the first repair evaluation value and the second repair evaluation value to obtain a target defect layout; and repairing the target defect layout in the mask design layout to obtain a repaired mask layout.

The invention relates to the technical field of optical waveguide master mask manufacturing processes, and discloses a monitoring and correcting method and system for an optical waveguide master mask etching process, and the method comprises the steps: setting a plurality of monitoring regions and corresponding monitoring point positions, and collecting the real-time monitoring data of each monitoring point position; inputting real-time monitoring data into the etching analysis model to obtain a real-time etching coefficient of each monitoring point; judging whether correction is needed or not according to the real-time corrosion coefficient, and if yes, determining a plurality of local defect areas and generating a correction instruction; a correction instruction is issued, a feedback time node is set, a correction data packet of each local defect area is collected according to the feedback time node, a correction evaluation value is generated, whether the local defect area is corrected again or not is judged according to the correction evaluation value, and real-time monitoring and accurate correction of the etching process are effectively achieved. The problems that in a traditional etching process, defects are known after being perceived due to lack of real-time monitoring, and resources are wasted due to global reworking are solved.

The invention discloses a defect detection method and system for an electronic display panel, and is applied to the technical field of computer vision, and the method comprises the steps: obtaining image data of a to-be-detected display panel; inputting the image data into a pre-trained neural network model to obtain the position and the type of the defect area of the display panel; wherein the neural network model comprises a backbone network, a neck network and a detection head, and the backbone network is used for performing feature extraction on the image data to obtain a plurality of initial feature maps; the neck network is used for performing feature interaction on the plurality of initial feature maps to obtain target feature maps respectively corresponding to the plurality of initial feature maps; and the detection head is used for performing target detection based on the target feature maps corresponding to the plurality of initial feature maps to obtain the position and the type of the defect area of the display panel. The defect detection precision of the display panel can be improved.

The invention provides a strip steel defect detection method and device and electronic equipment, and the method comprises the steps: firstly detecting a defect region in a strip steel surface image, cutting out a defect region block, recording the position coordinate of the defect region block, setting a defect classification model, dividing a roller mark defect and a stain defect into the same target category, and carrying out the detection of the defect. And a defect classification model is adopted to determine a defect category corresponding to each defect area block, then position periodical detection is performed on the defect area blocks in the target category, the defect areas conforming to the position periodicity are determined as roll mark defects, and the defect areas not conforming to the position periodicity are determined as stain defects. According to the scheme, stain defects, roll mark defects and other various types of defects can be accurately distinguished in an image processing mode under the condition that stains on the surface of the strip steel are not wiped, so that the defects caused by the fact that the stains on the surface of the strip steel need to be removed firstly and then the defect types need to be detected in the prior art are overcome; and the detection of the roller mark defects is not interfered by other defect types, so that the detection accuracy is higher.