Application of neural network in intelligent sampling inspection of inventory material defects
The existing intelligent sampling process of the inventory material defect has the problems of low accuracy,long problems.To solve these problems,a kind of neural network is applied to inventory material defects in the intelligent sampling method is put forward.On the basis of collecting stock image,the wave-let threshold is used to de-noise the image,and the image reconstruction is realized by quantization and in-verse wavelet transform.The correlation analysis of pixels is carried out to extract the target object and the corresponding scene information and describe the local features of the defect images of inventory materials,The sample data is expanded by image enhancement,and the intelligent sampling of inventory defects is re-alized by using the convolutional neural network optimized by genetic algorithm.The experiment results show that the neural network can quickly and accurately realize the automatic sampling of inventory defects.
the neural networkinventory material defectsintelligent sampling inspectionwavelet de-noisingfeature extraction
NETL
NSTL
万方数据
