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。
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 advanc
作者
高琳明
徐风
李享
徐姗姗
窦立君
GAO Linming;XU Feng;LI Xiang;XU Shanshan;DOU Lijun(College of Information Sciences and Technology,Nanjing Forestry University,Nanjing 210037,China)
出处
《林业工程学报》
CSCD
北大核心
2019年第4期99-106,共8页
Journal of Forestry Engineering
基金
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)
