[1]陈玉思,刘赟.基于脉冲神经网络的织物瑕点检测算法[J].泉州师范学院学报,2019,(06):39-44.
 CHEN Yusi,LIU Yun.Fabric Defect Detection Algorithm Based on Spiking Neural Network[J].,2019,(06):39-44.
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基于脉冲神经网络的织物瑕点检测算法()
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《泉州师范学院学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年06期
页码:
39-44
栏目:
数学·计算科学
出版日期:
2019-12-15

文章信息/Info

Title:
Fabric Defect Detection Algorithm Based on Spiking Neural Network
文章编号:
1009-8224(2019)06-0039-06
作者:
陈玉思刘赟
泉州师范学院 数学与计算机科学学院, 福建 泉州 362000
Author(s):
CHEN Yusi LIU Yun
School of Mathematics and Computer Science, Quanzhou Normal University,Fujian 362000,China
关键词:
织物 瑕点检测 脉冲神经网络 图像处理 权重矩阵
Keywords:
fabric defect detection spiking neural network(SNN) image processing weight matrix
分类号:
TP183
文献标志码:
A
摘要:
针对纺织业中织物瑕点检测尚未达到自动化、智能化,提出一种新的织物检测算法——基于脉冲神经网络(SNN)的织物瑕点检测算法.该算法根据生物信息处理机制,把网络模型分成3层,分别为:接收层、中间层、输出层.接收层负责将图像信息转为神经元的输入序列; 中间层根据四个不同权重矩阵的输出信息判断是否属于四个方向上的边缘像素点; 输出层将汇总中间的输出信息并判断是否为边缘图像点; 最后根据特征值经mallat算法定位瑕点区域.通过实验分析和对比,其结果表明该算法具有较好的检测率.
Abstract:
In view of the lack of automation and intelligence in fabric defect detection, a new fabric defect detection algorithm based on spiking neural network(SNN)is proposed in this paper. According to the biological information processing mechanism, the network model is divided into three layers, namely, receiving layer, middle layer and output layer. The receiving layer is responsible for converting the image information into the neuron's input sequence; The middle layer determines whether the edge pixel points belong to four directions according to the output information of four different weight matrices. The output layer will summarize the intermediate output information and determine whether it is edge image point. Finally, the defect area is located by Mallat algorithm based on eigenvalues. Experimental analysis and comparison show that the algorithm has good detection rate.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-03-21
作者简介:陈玉思(1987-),男,助教, 福建泉州人,硕士,从事图形图像处理、虚拟现实研究.
基金项目:福建省中青年教师教育科研项目(JAT170476)
更新日期/Last Update: 2019-12-15