6 results about "Blob analysis" patented technology

The invention relates to the technical field of industrial quality inspection, provides a wafer defect analysis method and electronic equipment, and is used for improving the wafer defect detection efficiency and precision. For a wafer whose appearance defect detection yield is lower than a preset yield threshold, mapping graphs corresponding to detection results of each core particle on the wafer are used as input of a defect segmentation model, independent defect areas of different defect types are identified, blob analysis is carried out on each defect area by using a computer vision technology, defect geometric information is obtained, and the defect geometric information is used as a defect segmentation model. According to the method, the defect geometric information of the wafer is obtained, whether the wafer has process abnormity or not is judged according to the comparison result of the defect geometric information and the defect card control parameters corresponding to the defect types, AI reasoning based on deep learning and traditional CV card control are combined, generalization of AI and accuracy of CV are fully utilized, and the defect detection precision and efficiency are improved.

This invention relates to the field of image processing technology, specifically to a method for detecting through-hole chucks that integrates mask positioning and AI re-inspection. The method involves preprocessing and contour detection of the original workpiece image to achieve coarse positioning of the through-hole region; constructing a multi-layer mask based on the precise geometric information of the through-hole; extracting candidate defect regions through mask subtraction and bitwise AND operations; performing Blob analysis on the candidate defect regions; and conducting multi-dimensional feature analysis and screening based on the physical prior conditions of the chuck to obtain traditional detection judgment results; inputting the through-hole region image into a deep learning model for re-inspection to obtain deep learning detection results; and performing a joint judgment based on the traditional detection judgment results and the deep learning detection results to output the final defect judgment result. This invention achieves precise positioning of the through-hole through multi-layer mask operation and, combined with the joint decision-making of Blob analysis and AI re-inspection, significantly improves the accuracy and robustness of through-hole chuck defect detection while reducing the false detection rate.

This invention relates to the field of industrial defect detection technology, and proposes a cross-scale industrial surface defect detection method and system, including the following steps: acquiring template image I a And the industrial product image to be inspected I b And preprocess it; based on the Blob algorithm, the preprocessed image I a I b Feature extraction is performed to obtain image features I'. a 、I' b The image I is estimated using a Homography estimation neural network. a and I b and image features I' a and I' b Perform alignment calibration separately; for the aligned and calibrated image I a I b The difference image is processed by subtraction, and a sliding window is used to filter noise from the difference image to obtain the industrial surface defect detection result. This invention introduces a Blob analysis algorithm to locate small-scale defects in multi-scale defects, and combines it with a Homography estimation neural network to align and calibrate the standard sample image used for the defect sample to be detected, so as to achieve better difference results.

The present application provides a kind of defect evaluation method of flexographic first piece, including character detection and dirty detection, wherein the character detection includes the following steps: S1: based on the character position of electronic sample and flexographic first piece corresponding construction electronic sample character library and flexographic first piece character library;S2: respectively input the pre-constructed depth evaluation network of electronic sample character and flexographic first piece character, obtain character detection result, complete character detection;The dirty detection includes the following steps: the image of flexographic first piece is subjected to time-frequency conversion, Gaussian filtering and Blob analysis, to obtain dirty detection result, complete dirty detection;According to character detection result and dirty detection result, obtain the defect evaluation result of flexographic first piece.The present application provides a kind of defect evaluation method of flexographic first piece, solves the problem that current flexographic first inspection adopts artificial quality inspection and is easy to produce misjudgment.

The application discloses a kind of element pin detection methods based on machine vision, comprising the following steps: step one: using industrial camera obtains the 3D point cloud of the detected element including pin;Step two: based on the 3D point cloud obtained in step one, obtain height map;Step three: in height map, select a height reference value, and form a selection interval on the height reference value according to the set height difference value;Step four: the point cloud of the sampling point in the selection interval determined in step three is used to form a gray scale image;Step five: in the gray scale image, the center of gravity of the pin is obtained using Blob analysis algorithm and center of gravity algorithm;Step six: use the center of gravity of the pin obtained in step five to judge defects, so use height map to set selection interval to select effective point cloud, so noise point cloud can be filtered, avoid noise point cloud adversely affect gray scale image, to form the real gray scale image of pin, and then judge the defects of pin.

The application provides an online quality sensing method for multiple types of battery pieces in a flower basket, mainly including the following three steps: S1, three-camera-based image acquisition and splicing, S2, based on Blob analysis and morphological principles, obtaining the multiple types of battery piece regions in the flower basket, and S3, matching the characteristic value parameters of the finished and semi-finished battery piece regions, for the obtained finished and semi-finished battery piece regions, the characteristic values such as the center of gravity, length and width are obtained through region decomposition and other methods, and the characteristic values are screened to remove other interference regions, to obtain accurate finished and semi-finished battery piece regions, and then the threshold range of the related characteristic values set in the algorithm is matched, to judge whether the selected finished and semi-finished battery piece regions meet the set standard, so that the online quality sensing of the multiple types of battery pieces in the flower basket is realized quickly and stably.