Defect classification method based on improved particle swarm wavelet neural network

Abstract

The invention belongs to the technical field of machine vision detection, and particularly relates to a defect classification method based on an improved particle swarm wavelet neural network. The problems that a traditional BP neural network algorithm is prone to convergence and prematurity, and cause a local minimum value and the like are solved. The method comprises the following steps: loadingan original image, carrying out graying and median filtering processing, segmenting the image, calculating a defect feature vector, initializing a particle swarm, calculating a target fitness value,evaluating each particle, updating the position and speed of each particle, checking whether the requirement is met, outputting an optimal solution, and finally carrying out defect classification on the image. According to the method, a variation factor is added, so that the generalization capability of the algorithm is ensured. A nonlinear weight factor is set, and a target of flexible adjustmentof global search and local search is realized. A global extreme value of Gaussian weighting is introduced, convergence of the global extreme value to the optimal solution direction is facilitated, defects can be classified quickly and accurately, the classification result is more accurate, and the efficiency is higher.

Description

technical field [0001] The invention belongs to the technical field of machine vision detection, and specifically relates to a defect classification method based on an improved particle swarm wavelet neural network. Background technique [0002] The full name of polarizer is polarizer, which is an important part of liquid crystal display imaging. With the development of the economy and the progress of science and technology, liquid crystal displays have been widely used in various industries. The imaging of the liquid crystal display requires two polarizers to be closely attached to the liquid crystal glass. The appearance defects of the polarizers have a direct impact on its quality. In the production process of polarizers, due to industrial technology and production equipment, defects such as scratches, stains, and missing corners will inevitably occur. Because most of the defects are very small, it is difficult for the naked eye to distinguish good from bad when inspect...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems that the traditional BP neural network algorithm is prone to premature convergence and fall into local minimum, etc., and provides a defect classification method based on improved particle swarm wavelet neural network

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