Solder joint defect detection method of ic components based on mixed Gaussian model

Abstract

The invention discloses an IC element solder joint defect detection method based on a Gaussian mixture model. The method comprises following steps: initializing template quantities, weights, the mean value and the variance of the Gaussian mixture model, and establishing a frequency distribution graph according to the height and width of a training picture; acquiring an IC solder joint training picture from a training sample, and updating the Gaussian mixture model and the frequency distribution graph; determining whether the training sample finishes training, and calculating a defect degree threshold of the training sample if the training sample finishes training; collecting a picture of an IC element solder joint to be detected, then calculating the defect degree of the picture with the cooperation of the trained Gaussian mixture model and the frequency distribution graph; and comparing the defect degree of the picture and the defect degree threshold of the training sample so as to obtain a detection result of the IC element solder joint. The method is less in calculation amount, rapid in detection speed and high in accuracy, can effectively detect insufficient solder defects of the IC element solder joint, and can be widely applied to the field of IC element solder joint defect detection.

Description

technical field [0001] The invention relates to the application field of digital image processing, in particular to a method for detecting solder joint defects of IC components based on a mixed Gaussian model. Background technique [0002] PCB (Printed Circuit Board) defect detection is a hot spot in the application of automatic optical inspection (AOI), which has received more and more attention in recent years. At present, the main way is to detect the image of the PCB component through the CCD and then process it to realize the defect detection. In actual use, the situation encountered by printed circuit boards is very complicated, and the images of PCB components collected by CCD often have varying degrees of changes and irregularities, such as: uneven light intensity, changes in light angles, and images collected by CCD cameras. There is a certain angle of deflection, the size of components is getting smaller and smaller, and the density of components in the PCB board ...

AI Technical Summary

Problems solved by technology

However, due to the small size of the solder joints of IC components, the high density of solder joints, and the difficulty in collecting samples of solder joints, it is difficult for the current classifier-based method to obtain a good classification effect in the detection of solder joints of IC components.

In addition, the online detection method has strict time requirements, and the relatively mature classifiers such as neural networks have a large amount of calculation, which is difficult to meet the time requirements of online monitoring.

At the same time, although some people have proposed an IC component solder joint detection method based on pixel point modeling of a single Gaussian model, this method has a fast detection speed, but its accuracy is low, and it cannot be applied in actual production.

In general, the current detection methods cannot effectively, accurately and quickly detect the solder joint defects of IC components

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