[1]高琳明,徐风,李享,等.基于深度学习特征和非线性支持向量机的板材表面缺陷识别方法[J].林业工程学报,2019,4(04):99-106.[doi:10.13360/j.issn.2096-1359.2019.04.015]
 GAO Linming,XU Feng,LI Xiang,et al.Board surface defects recognition method based on deep learning features and non-linear support vector machine[J].Journal of Forestry Engineering,2019,4(04):99-106.[doi:10.13360/j.issn.2096-1359.2019.04.015]
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基于深度学习特征和非线性支持向量机的板材表面缺陷识别方法()
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《林业工程学报》[ISSN:1001-8081/CN:32-1160/S]

卷:
4
期数:
2019年04期
页码:
99-106
栏目:
装备与信息化
出版日期:
2019-07-09

文章信息/Info

Title:
Board surface defects recognition method based on deep learning features and non-linear support vector machine
文章编号:
2096-1359(2019)04-0099-08
作者:
高琳明徐风李享徐姗姗窦立君
南京林业大学信息科学技术学院,南京 210037
Author(s):
GAO Linming XU Feng LI Xiang XU Shanshan DOU Lijun
College of Information Sciences and Technology, Nanjing Forestry University, Nanjing 210037, China
关键词:
深度学习 特征提取 支持向量机 板材缺陷
Keywords:
deep learning feature extraction SVM board surface defects
分类号:
TP399
DOI:
10.13360/j.issn.2096-1359.2019.04.015
文献标志码:
A
摘要:
深度学习是一种有效的特征学习方法,具有很强的自主学习能力。研究了基于深度学习特征与非线性支持向量机(NSVM)分类算法相结合的板材表面缺陷识别方法。首先,针对深度学习模型需要海量训练数据的特性,使用旋转剪切的方法对采集到的原始板材表面缺陷图像进行数据扩增; 其次,使用扩增后的板材表面缺陷图像数据集对笔者提出的深度卷积神经网络(CNN)模型进行训练,并使用训练好的网络提取不同种类缺陷图像的深度特征; 然后,为了消除深度特征中的冗余数据,并增强数据的表达能力,运用基于1范数的非贪婪主成分分析(Non-greedy PCA-L1)算法对板材的深层语义特征进行特征降维和特征增强; 最后,运用增强后的深度特征训练NSVM模型,并使用训练好的NSVM模型对原始板材表面缺陷图像的测试集进行分类。实验结果表明,笔者提出的识别方法具有较好的鲁棒性和实用性,可取得目前较好的分类效果,针对结疤、压痕和无损3种板材表面缺陷识别率可达99%以上。
Abstract:
Deep Learning(DL)is one of the most efficient feature learning methods in the field of computer vision, which has a strong autonomous learning ability.As one of the most representative DL models, the Convolutional Neural Network(CNN)model plays a fundamental role in the image processing domain.Many state-of-the-art image processing models are developed based on the CNN model.However, the CNN model usually polarizes the output values, and some of which are close to +1, while the other parts are close to -1.In practice, it is more inclined to output the attribution of certain samples in the form of a more extensive probability.In order to improve the generalization of the recognition model and make the recognition model more reliable, in this paper, the board surface defects recognition problem was investigated based on the combination of the deep learning features and the Non-linear Support Vector Machine(NSVM)classifier.Specifically, three kinds of common surface defect images were firstly collected, which were nodule images, indentation images and nondestructive images.Then, three steps were proposed for the board surface defects recognition: the collected board images were used to train a novel modified CNN classification model, and the trained CNN model was used to extract deep features of these three different surface defect images in the validation set.Then, in order to enhance the expressive ability of these depth features, the Non-Greedy Principal Component Analysis with L1-norm(Non-greedy PCA-L1)algorithm was used to deduce their dimension and obtain some more compact deep features.After that, the enhanced features were used to train the NSVM classifier.Finally, the trained NSVM classifier was used to determine the images' category with board surface defects.Experiment results demonstrated that the improved CNN model was robust and practical, and a new the-state-of-art recognition accuracy can be achieved.For these three kinds of surface defects, the recognition rate was up to 99%.In the future, it is expected to use some advanced algorithms to recognition defects on the board surface, such as the semi-supervised method.In addition, the training of CNN model requires a large number of labeled images, so the recognition method based on generation networks is also a promising research direction.The main source code and data involved in this paper will be published on the GitHub after the paper is published.

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备注/Memo

备注/Memo:
收稿日期:2018-10-11 修回日期:2019-03-24
基金项目:国家自然科学基金(31670554); 江苏省自然科学基金(BK20161527); 南京林业大学青年科技创新基金资助项目(CX2016024)。
作者简介:高琳明,女,讲师,研究方向为图像处理、模式识别和机器学习等。E-mail: glm@njfu.edu.cn
更新日期/Last Update: 2019-07-10