Weld Defects Images Classification and Recognition Based on Sparse Deep Belief Network

LIU Meng-xi; WANG Zheng; SONG Jiu-xu; JU Yong-feng; WU Xiao-meng

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Weld Defects Images Classification and Recognition Based on Sparse Deep Belief Network

更新时间：2026-09-04

- Weld Defects Images Classification and Recognition Based on Sparse Deep Belief Network
- Vol. 35, Issue 9, Pages: 59-63(2018)
- 作者机构：
  
  1. 长安大学电子与控制工程学院,陕西,西安,710064
  2. 西安石油大学机构光电油气测井与检测教育部重点实验室,陕西,西安,710065
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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刘梦溪, 王征, 宋久旭, 巨永锋, 武晓朦. 基于稀疏深度置信网络的图像分类识别研究[J]. 微电子学与计算机, 2018, 35(9): 59-63.

LIU Meng-xi, WANG Zheng, SONG Jiu-xu, et al. Weld Defects Images Classification and Recognition Based on Sparse Deep Belief Network[J]. 2018, 35(9): 59-63.
刘梦溪, 王征, 宋久旭, 巨永锋, 武晓朦. 基于稀疏深度置信网络的图像分类识别研究[J]. 微电子学与计算机, 2018, 35(9): 59-63. DOI：

LIU Meng-xi, WANG Zheng, SONG Jiu-xu, et al. Weld Defects Images Classification and Recognition Based on Sparse Deep Belief Network[J]. 2018, 35(9): 59-63. DOI：

摘要

针对工业生产中X射线获取的焊缝图片缺陷难以被识别的问题

构建了一个深度置信网络模型

该模型由三层受限玻尔兹曼机组成

由于将稀疏约束引入深度学习模型的算法中

使得焊缝缺陷信息能够获取到有效的目标稀疏表示

以更为简洁有效的训练实现对焊缝缺陷信息的识别.通过分层网络模型对比实验表明

稀疏深度置信网络能够提高焊缝缺陷图像识别的正确率

实现更为精准的图像信息分类

在有效避免过拟合现象的发生的同时

提升了模型对于焊缝缺陷识别的性能.

Abstract

Because weld defects images obtained by X-ray are difficult to be identified in industrial production

a Deep Belief Network (DBN) model composed of three-layer Restricted Boltzmann Machine(RBM) is proposed.Due to the sparse constraint introduced into the depth learning model algorithm

the weld defects information can obtain the target sparse representation in more effective way and the weld defects information can be identified through more concise and effective training. The comparison experiment of layered network model shows that the Sparse Deep Belief Network can ensure the accuracy of weld defects images recognition and classification while improving the performances of Deep Belief Network by avoiding the overfitting.

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references

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Jia, Yangqing, Shelhamer, et al. Caffe: Convolutional Architecture for Fast Feature Embedding[C]// Proceedings of the 22nd ACM international conference on Multimedia. ACM New York, NY, USA 2014: 675-678.

Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks[C]// European Conference on Computer Vision. Springer, Cham, 2014: 818-833.

Secaucus, NJ, USA Bishop C M. Pattern Recognition and Machine Learning (Information Science and Statistics)[M]. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006: 542-546. DOI: 542

Shao J, Du D, Chang B, et al. Automatic weld defect detection based on potential defect tracking in real-time radiographic image sequence[J]. Ndt % E International, 2012, 46(1):14-21. DOI: 14

张立民, 刘凯.基于深度玻尔兹曼机的文本特征提取研究[J].微电子学与计算机, 2015, 32(02):142-147. DOI: 142

欧阳宁, 李子, 袁华, 陈利霞.基于自适应稀疏表示的多聚焦图像融合[J].微电子学与计算机, 2015, 32(06):22-26. DOI: 22

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