1577 results about "Grayscale" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention belongs to the technical field of image processing, and particularly relates to a flour impurity screening method and system based on image processing, and the method comprises the steps: obtaining a surface grayscale image; obtaining a texture response value according to the gray scale standard deviation in the pixel point neighborhood and the local information entropy; adjusting the basic gradient amplitude by using the texture response value to obtain an enhanced gradient value; obtaining an impurity probability value according to the difference of the structure tensor characteristic values and the enhanced gradient value; constructing a background substrate by using morphological reconstruction, and obtaining a significance index according to a difference value between the impurity probability value and the background substrate; and performing segmentation and connected domain screening according to the significance index. While impurity edge signals are reserved, dust noise and natural fluctuation interference of flour are effectively filtered out, and the accuracy of detecting near-color impurities on the surface of the flour is improved.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention discloses a geological map-oriented breakpoint repairing and closed curve reconstruction method and system and a medium. The method comprises the following steps of: preprocessing an original geological map to obtain a grayscale image; carrying out binarization and morphological closed operation processing to obtain a grayscale image of the edge of the smooth contour curve; performing pixel skeletonization to obtain a pixel-level skeleton diagram; performing secondary edge extraction, connected domain marking and endpoint statistics on the pixel-level skeleton diagram, and screening out a non-closed curve and an endpoint set; carrying out nearest neighbor retrieval by adopting KD-Tree to obtain a candidate pairing set of each end point; performing priority pairing in combination with the spatial distance and the direction smoothness; and generating a transition line segment according to a pairing result to supplement the fracture part of the non-closed curve. According to the method, non-closed breakpoints generated by scanning or drawing errors in the geological map can be automatically restored, and a closed curve with topological integrity and a smooth boundary is generated.

A display device includes a pixel emitting light based on input image data, an age data calculator configured to generate age data of a light emitting element included in the pixel in consideration of a power voltage supplied to the pixel, and a compensator configured to output age compensation data by determining a grayscale compensation value corresponding to an input grayscale of the input image data, based on the age data, and applying the grayscale compensation value to the input image data.

The invention discloses an ultra-thin glass defect detection system and positioning method based on line scanning and phase deflection, and the system comprises a line scanning camera module, a distortionless phase deflection detection module, a high-magnification microscope module and a central processing unit. The line scanning camera module is used for collecting a 2D grayscale image of the ultrathin glass; the distortionless phase deflection technology detection module is used for collecting a surface height map of the ultra-thin glass; the high-magnification microscope module is used for carrying out depth positioning on the interlayer defect judged by the central processing unit; the central processing unit is used for receiving the 2D gray level image and the surface height map, executing an image registration and defect classification algorithm and sending a depth positioning control instruction to the high-magnification microscope module, a multi-module cooperative work detection system is constructed, the detection efficiency and the detection precision are both considered, and data support is provided for production process improvement.

The invention provides a steel plate surface defect detection method and system based on multi-source data fusion, and relates to the technical field of steel production automatic detection.The method comprises the steps that water stain pretreatment is conducted on the surface of a steel plate, the residual humidity is reduced to be below a preset low humidity threshold value, and a dry surface is obtained; based on the dry surface, collecting multi-source data through a 3D laser line scanning camera and a 2D industrial line scanning camera which are synchronously triggered, obtaining line scanning laser point cloud data, a line scanning reflectivity gray level image, a line scanning depth gray level image and an area array reflectivity color image, and establishing a space corresponding relation among the multi-source data; and forming a multi-source data set based on the spatial correspondence among the multi-source data. According to the method, a high-precision and high-robustness steel plate surface defect detection system covering the whole detection process is constructed by eliminating water stain interference, unifying a multi-source data reference, optimizing data quality, accurately identifying defects and realizing automatic feedback and data closed loop.

The invention belongs to the technical field of image processing, and particularly relates to an aluminum bar quality detection method and system based on machine vision, and the method comprises the steps: obtaining a gray image of the surface of an aluminum bar; constructing a structure tensor for describing the distribution of the pixel points in the local gradient direction, and obtaining gradient coherence indexes of the pixel points according to feature values of the structure tensor; according to the gradient coherence index, the gray value of the pixel point and the gray value mean value and the gray value standard deviation in the neighborhood, obtaining coherence weighted saliency of the pixel point; according to the coherence weighted saliency and the coherence weighted saliency of all the pixel points in the pixel point neighborhood, acquiring a defect probability index of the pixel points; and performing aluminum bar surface quality discrimination according to the defect probability index. According to the method, through combination of gradient coherence and local brightness statistical characteristics, interference of specular reflection light spots on the surface of the aluminum bar and wiredrawing texture noise is effectively inhibited, and accuracy of detection of weak defects such as scratches is improved.

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

The invention relates to the technical field of machine vision and wind power detection, and discloses a surface defect identification method and system for a rotor core of a wind driven generator, and the method comprises the steps: collecting an original image of the surface of the rotor core, and carrying out the gray mapping, and obtaining a single-channel gray image; carrying out frequency domain periodic texture suppression and local histogram equalization processing with contrast limitation on the single-channel grayscale image to obtain an enhanced feature image; constructing a background fitting model for the enhanced feature image and performing differential operation to obtain a background differential image; performing adaptive segmentation and morphological refining based on a texture direction according to the background difference image to obtain a refined defect connected domain; local gray level distribution is extracted, sub-pixel-level geometric feature calculation is carried out, and defect geometric attribute data are obtained; and spatial clustering and grading evaluation are carried out according to the data, and a final defect distribution map is determined. According to the method, periodic texture interference can be effectively suppressed, and sub-pixel-level precise positioning and intelligent grading of the tiny defects are realized.

The invention provides a micro-expression recognition method based on brain-like vision. The method comprises the following steps of: dividing an event flow output by an event camera according to fixed time steps, constructing a multi-time-step event frame sequence, and synchronously acquiring an initial grayscale image as static texture prior; extracting high-dimensional features of each time step event frame through an event encoder sharing parameters, and generating a corresponding pixel-level motion vector field; performing deformation sampling on the initial grayscale image by using the motion vector field to generate a reconstructed image of the target time step; and splicing the initial grayscale image and the deformed image in a channel dimension, inputting the spliced image into an image refining network for fusion optimization, and outputting a continuous grayscale image sequence. According to the method, the asynchronous event data output by the event camera can be effectively converted into a high-quality continuous image sequence, and the time sequence modeling precision and robustness of micro-expression recognition are improved.

The invention discloses an AI-based logistics document intelligent identification and filling system, and the system comprises an image collection module which is used for obtaining the image information of a logistics document, supporting a plurality of image input modes, such as scanner, mobile phone photographing, camera capturing and the like, carrying out the preprocessing of a collected image, including the operation of image enhancement, denoising, graying, binaryzation and the like, and obtaining the image information of the logistics document; the image quality is improved, and preparation is made for follow-up recognition; the document type identification module is used for carrying out document type judgment on the preprocessed document image based on a convolutional neural network (CNN) algorithm in deep learning, and can automatically identify common logistics document types; the invention relates to the technical field of logistics information, and the AI-based logistics document intelligent identification and filling system can efficiently and accurately identify various logistics documents, realize intelligent filling and improve the automation level and accuracy of logistics document processing.

The invention relates to the technical field of wiring robot system fault detection, in particular to a transformer wiring robot system fault intelligent detection method. The method comprises the following steps: acquiring an image in a wiring process and graying the image to obtain a grayscale image; obtaining candidate pixel points in the grayscale image and a pixel point sequence of the candidate pixel points; acquiring a local gradient amplitude change feature value and a local gradient direction change feature value of each neighbor pixel point, and further acquiring initial edge feature credibility of the candidate pixel points; obtaining the final edge feature credibility based on the initial edge feature credibility of each marked pixel point of one candidate pixel point; performing clustering analysis based on the final edge feature credibility of each candidate pixel point in a gray level image to obtain a low threshold value and a high threshold value of the gray level image; and carrying out edge detection on the grayscale image by using the low threshold and the high threshold, and identifying the abnormity of the wiring robot. According to the invention, the wiring abnormity of the wiring robot can be effectively processed.

The invention discloses an infrared small target detection method based on comparative learning and frequency gradient feature fusion, and aims to improve the detection precision. A current method faces three challenges that the contrast of an infrared image is low, space information is limited and is interfered by clutters, so that global context is missing, and robustness is insufficient; target structure features are weak and are similar to background gray, and texture extraction is difficult; an image background is complex, a target is tiny, missing detection and false alarm are caused frequently, and the distinguishing capacity of a foreground and the background is affected. For the first problem, a frequency attention perception fusion module is designed to improve semantic consistency and positioning precision. In order to solve the second problem, a textural feature enhancement module is designed to enhance textural feature representation. In order to solve the third problem, a temperature sensing comparison learning module is designed to improve the foreground and background distinction degree. In combination with the three designs, the model designed by the invention can accurately detect the small target in the infrared image, and is worthy of vigorous popularization.

The invention relates to the technical field of measurement and detection, in particular to a test sieve calibration method based on machine vision. Comprising the following steps: acquiring a test screen image by a microscope, firstly performing graying processing on an original image, and converting a color image into a grayscale image; the method comprises the following steps: carrying out binarization processing on a grey-scale image, carrying out binarization on the image, setting a grey-scale value of a pixel point on the image to be 0 or 255, enabling the whole image to present an obvious visual effect which is only black and white, and better analyzing the shape and the contour of an object through binarization; and performing connected domain analysis on the binary image, finding a pixel point to which each sieve hole belongs, endowing each pixel point with a label through the connected domain analysis, and forming a connected domain by the pixel points with the same label value so as to realize segmentation of the region of interest. The method is applied to the measurement calibration work of the test sieve, the working efficiency of verification and calibration personnel can be greatly improved by using the method, and human resources are saved.

The invention belongs to the technical field of image processing, and particularly relates to an MBR flat membrane surface physical damage visual identification method and system, and the method comprises the steps: carrying out the low-pass filtering of a microscopic gray image, so as to melt a micropore background; calculating a texture variation index based on the outlier degree of the gray values of the neighborhood pixel points and the neighborhood gray dynamic contrast gain; determining a damage aggregation weight by using a space attenuation accumulation value, and eliminating isolated noise points; the texture variation index and the damage aggregation weight are fused through a self-adaptive gating function, and damage confidence is generated; and finally marking a connected damage region based on statistical threshold binarization and morphological processing. According to the method, dense micropore interference can be effectively inhibited, and the physical damage identification precision is improved.

The invention relates to the technical field of image processing, in particular to a non-contact measurement method and system for the thickness of a cable insulation layer, and the method comprises the steps: constructing an energy field according to a gradient amplitude in a cable cross section grayscale image; initializing inner and outer contour lines of the edge of the insulating layer in the grayscale image; performing short-term iterative evolution on the inner and outer contour lines by using an active contour model to quantify the motion uncertainty of each contour point; adaptive adjustment is carried out on the inner contour line and the outer contour line according to motion uncertainty; on the basis of the adaptive adjustment, continuing iterative evolution until the inner contour line and the outer contour line converge; and extracting geometric parameters based on the converged inner and outer contour lines to determine the thickness of the cable insulation layer. According to the technical scheme, accurate segmentation of the inner edge and the outer edge of the cable insulation layer can be achieved, and therefore the thickness of the cable insulation layer can be accurately measured.

A camera calibration method based on an integrated dynamic dispersion-enhanced particle swarm optimization algorithm includes: acquiring multiple images of a calibration board of different angles and converting them into grayscale images, detecting Harris corner points, and solving sub-pixel coordinate; estimating, by using the sub-pixel coordinates and a distortion camera model, initial values of camera intrinsic parameters through Zhang's camera calibration method; calibrating the camera intrinsic parameters, and calculating fitness values of particles; determining whether iteration termination condition is met, whether the fitness values of the particles have reached a convergence condition, and whether algorithm is trapped in a local optimum, to thereby determine whether a maximum number of iterations is reached or a specific fitness threshold is met; and outputting camera parameters corresponding to a global optimal solution of the particles when the maximum number of iterations is reached or the specific fitness threshold is met.

The invention discloses a Shack-Hartmann wavefront detector centroid calculation method and related equipment, and relates to the field of optical measurement, and the method comprises the steps: obtaining a light spot array grayscale image, detecting the local maximum value of each light spot, and dividing sub-aperture image regions corresponding to micro lenses one by one according to the local maximum value; extracting a gray matrix of each sub-aperture image region, respectively determining one-dimensional regions of a light spot main lobe in row and column directions, and intersecting to obtain a main lobe region; a main lobe area is deducted from the sub-aperture image area to form a threshold calculation area, and the maximum gray value of the threshold calculation area is selected as a threshold; and performing threshold reduction on pixels in the sub-aperture image area, setting a negative value to zero, and calculating the position of the mass center of the light spot based on the processed image. According to the method, sub-aperture adaptive threshold and local background removal are realized, weak light spot information loss caused by a unified threshold is avoided, and the precision of centroid calculation and wavefront restoration is improved.

The invention discloses a method for performing visual inspection on surface forming quality characteristics of a welding seam by utilizing three-dimensional reconstruction, which comprises the following steps of: S1, performing uniform-speed scanning on the welding seam along the direction of a welding bead by using linear structured light emitted by a linear laser, and performing image acquisition on linear structured light stripes formed on the surface of the welding seam by using an industrial camera; s2, converting the acquired RGB image into a gray level image frame by frame, and carrying out image distortion correction and image denoising processing; s3, carrying out ROI (Region of Interest) positioning on the formed line structured light stripe image on the surface of the welding seam by adopting a pixel point gray level distribution calculation method; s4, carrying out image segmentation on the ROI region of the weld line structured light stripe image; and S5, carrying out sub-pixel-level center line extraction on the ROI of the weld line structured light stripe image by adopting a gray extreme value weighted centroid method. Automatic detection of welding seam forming quality characteristics such as laser welding and electric arc welding can be achieved, and the operation efficiency of a production line and the welding seam quality detection precision can be greatly improved.

The invention relates to the technical field of image processing, in particular to an aluminum alloy die casting surface defect detection method based on machine vision. The method comprises the steps of obtaining a grayscale image of the surface of a die casting; constructing a Gaussian scale space of the grayscale image, and obtaining scale images under a plurality of scales; calculating a defect saliency index corresponding to each pixel point in the grayscale image based on the multi-scale image, and generating a defect saliency map; and performing threshold segmentation on the defect saliency map to obtain a segmented image, performing morphological processing identification on the segmented image, and determining the position of a surface defect area. According to the invention, missed detection and false detection in the surface detection process of the aluminum alloy die casting can be reduced.

The invention relates to the technical field of glass bottle detection, and discloses a glass bottle bottom defect detection method which comprises the following steps: building a detection platform, and adopting a multi-view imaging module of an annular LED and oblique light supplement combined light source, a vertical camera and 3-4 oblique cameras; after the to-be-detected glass bottle is positioned, synchronously collecting a bottle bottom full-view image set; graying, adaptive median filtering, CLAHE histogram equalization and Otsu binarization preprocessing are carried out on the image; extracting geometric and textural features of the image, inputting the geometric and textural features into an improved YOLOv5 model for identification and classification, and judging defects by combining with multi-view result fusion; and sorting the glass bottles according to a detection result and generating a traceable report. The identification rate of tiny defects is larger than or equal to 99%, the detection time of a single bottle is smaller than or equal to 0.5 second, and the method is compatible with glass bottles with the diameter of 30-100 mm, is suitable for large-scale quality control in the fields of food and beverage, medicine packaging and the like, and has extremely high practical value.

The invention discloses a low-illumination image quality improvement method based on self-adaptive stochastic resonance. The method is specifically implemented according to the following steps: step 1, synchronously acquiring a grayscale image and a color original image corresponding to the same image through two channels; step 2, preprocessing the grayscale image and the color original image acquired in the step 1; 3, self-adaptive stochastic resonance parameter calculation is carried out; step 4, performing dual-channel adaptive stochastic resonance processing on the preprocessed grayscale image and the preprocessed color original image according to adaptive stochastic resonance parameters to obtain an enhanced image; and 5, carrying out brightness component fusion on the enhanced image, and then carrying out image reconstruction and post-processing. According to the invention, the problem of poor image quality after low-illumination image enhancement in the prior art is solved.

The invention relates to the technical field of machine vision detection, in particular to an automobile part surface defect online detection method and system. The method comprises the following steps: acquiring a grayscale image, calculating the size of a structural element for each pixel based on a local Gaussian Laplacian operator response variance, filtering to obtain a substrate image according to the size of the structural element, and differentiating to obtain a texture image. Determining a Gabor scale and a gray-level co-occurrence matrix statistical direction by using the size, and extracting a cooperative direction gray-level co-occurrence matrix feature; and a weight is set based on the size and is subjected to weighted fusion with a multi-scale rotation invariant local binary pattern feature to generate a texture saliency map, and texture defects are judged. On the substrate image, taking the gray value as the height, and determining a neighborhood calculation curvature feature based on the size to detect the substrate defect. According to the scheme, the image scale can be adaptively analyzed, the background texture is effectively inhibited, and therefore different types of tiny defects such as scratches and pits can be reliably detected.

The invention belongs to the technical field of image processing, and particularly relates to an inonotus obliquus raw material granularity and uniformity detection method based on image analysis, and the method comprises the steps: obtaining an inonotus obliquus raw material gray scale image, and carrying out the preprocessing and binaryzation to obtain an adhesion body candidate object; screening particle core seed points through distance transformation in combination with an adaptive threshold strategy based on image statistical characteristics; calculating the attribution probability of a pixel to a seed point through a geodesic distance, achieving the soft segmentation of an adhesion region through a negative index model, and extracting a non-destructive segmentation line to obtain independent particles; and calculating morphological parameters of the particles, and obtaining a global uniformity index by combining the size dispersion and the shape regularity. According to the method, the accuracy of adhesion segmentation of irregular inonotus obliquus raw materials with complex textures is improved, and the robustness and accuracy of granularity detection are improved.

The present application relates to a kind of for the determination method of liquid alloy density in electrostatic suspension state in space station, after obtaining the photo of liquid alloy in electrostatic suspension state in space station, first photo is converted into pixel matrix and carries out gray scale processing, then deduct photo background by matrix operation.Further, the best binary grayscale threshold of pixel matrix is calculated, and pixel matrix is carried out binary processing.Then all boundary coordinates in pixel matrix are obtained by matrix operation, and each connected boundary is distinguished.Excluding bright spot boundary, the boundary coordinates and center coordinates of liquid alloy are obtained.Then the boundary equation of liquid alloy is obtained by using multi-order Legendre equation fitting liquid alloy boundary coordinates.Finally, the volume of liquid alloy is obtained by rotating integral to the boundary equation of liquid alloy, and then the density value of liquid alloy is obtained.The density value of the present application can be obtained by processing liquid alloy photo, and can be used for the determination of containerless liquid alloy density in electrostatic suspension in space station.

The invention relates to the technical field of image recognition, in particular to a cabinet board defect detection method based on machine vision, which comprises the following steps: acquiring a cabinet board surface gray level image, converting spatial domain pixel gray level value distribution into frequency domain spectrum data, calculating logarithmic spectrum amplitude, subtracting local average spectrum amplitude from the logarithmic spectrum amplitude, and calculating a cabinet board defect detection result. And extracting a spectral residual component. According to the method, the rough positioning coordinates of the edge of the plate are quickly locked by using the waveform abrupt change peak value, complex edge detection operation on all pixels of the whole image is avoided, a local region of interest is set based on the rough positioning coordinates, an edge linear equation is fitted, edge fitting is realized in a very small calculation region, and the edge detection accuracy is improved. According to the method, the lengths of the angular points and the diagonals are calculated by combining a linear equation, high-precision measurement of the length and width sizes and the verticality of the diagonals of the large-size plate can be achieved with extremely low calculation cost, the problem that the detection efficiency is low due to the fact that the size of the plate is too large is solved, and the squareness and the structural stability of the cabinet body during assembly are guaranteed.