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计算机系统应用:2019,28(3):140-145
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基于Fisher准则的深层卷积神经网络织物疵点检测
(浙江理工大学 信息学院, 杭州 310018)
Deep Convolutional Neural Network Fabric Defect Detection Based on Fisher Criterion
(College of Informatics and Electronics, Zhejiang Sci-Tech University, Hangzhou 310018, China)
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投稿时间:2018-09-03    修订日期:2018-09-26
中文摘要: 针对当前的算法无法满足具有周期性图案织物疵点分类检测,鉴于此,提出基于Fisher准则的深层卷积神经网络织物疵点检测算法.首先,利用深度可分离卷积设计小型的深层卷积神经网络(DCNN);其次,对DCNN网络的Softmax增加Fisher准则约束,通过梯度算法更新整个网络参数,得到深层卷积神经网络(FDCNN);最后,在TILDA和彩色格子数据集上分类率分别为98.14%和98.55%.实验结果表明:FDCNN模型既可以减小网络参数和降低运行时间,又可以提高织物疵点分类率.
Abstract:In view of the fact that the current algorithm can not meet the needs of fabric defect classification detection with periodic pattern characteristics, a deep convolutional neural network fabric defect detection algorithm based on Fisher criterion is proposed. First, a small Deep Convolutional Neural Network (DCNN) is designed by using depthwise separable convolution. Further, the Softmax loss function of DCNN adds Fisher criterion constraint and updates the whole network parameters through gradient algorithm to get Deep Convolutional Neural Network (FDCNN). Finally, the classification rates of TILDA and pink plaid fabric database were 98.14% and 98.55%. The experimental results show that the FDCNN model can not only effectively reduce network parameters and running time, but also improve fabric defect classification rate.
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史甜甜.基于Fisher准则的深层卷积神经网络织物疵点检测.计算机系统应用,2019,28(3):140-145
SHI Tian-Tian.Deep Convolutional Neural Network Fabric Defect Detection Based on Fisher Criterion.COMPUTER SYSTEMS APPLICATIONS,2019,28(3):140-145

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