机械制造与自动化2024,Vol.53Issue(6) :174-179.DOI:10.19344/j.cnki.issn1671-5276.2024.06.035

基于融合波动抑制的机械零件图像智能分类算法设计

Design of Intelligent Classification Algorithm for Mechanical Part Image Based on Fusion Fluctuation Suppression

纪永
机械制造与自动化2024,Vol.53Issue(6) :174-179.DOI:10.19344/j.cnki.issn1671-5276.2024.06.035

基于融合波动抑制的机械零件图像智能分类算法设计

Design of Intelligent Classification Algorithm for Mechanical Part Image Based on Fusion Fluctuation Suppression

纪永1
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作者信息

  • 1. 上海城建职业学院,上海 201415
  • 折叠

摘要

为提升图像分类效果,提出双目加权模型融合波动抑制的机械零件图像分类算法.对图像进行小波分解,获取高频分量,重构处理后的分量得到去噪图像.将去噪后的图像分解为拉普拉斯金字塔序列,通过平均梯度和区域能量融合系数融合图像,引入双目加权模型完成图像重构抑制融合系数波动.采用CNN提取图像特征,使用滤波器训练特征,引入集成学习策略获取分类标签,实现图像分类.实验结果表明:所提算法的融合系数波动较小,图像分类效果较好.

Abstract

To improve the image classification performance,a mechanical part image classification algorithm based on binocular weighted model fusion and fluctuation suppression is proposed.Wavelet decomposition on the image is performed to obtain high-frequency components,and the processed components are reconstructed to obtain a denoised image.The denoised image is decomposed into a Laplacian pyramid sequence,and by fusing the image through average gradient and regional energy fusion coefficient,a binocular weighted model is introduced to complete image reconstruction and suppress fusion coefficient fluctuations.With adoption of CNN to extract image features and filters to train features,ensemble learning strategies are introduced to gain classification labels,achieving image classification.The experimental results show that the proposed algorithm has small fluctuations in fusion coefficients and good image classification performance.

关键词

双目加权模型/融合波动抑制/机械零件/图像分类/小波变换/卷积神经网络

Key words

binocular weighted model/fusion fluctuation suppression/mechanical parts/image classification/wavelet transform/convolutional neural network

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出版年

2024
机械制造与自动化
南京机械工程学会 南京机电产业(集团)有限公司

机械制造与自动化

CSTPCD
影响因子:0.29
ISSN:1671-5276
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