| 韩英莉,洪英.带钢表面缺陷的一种在线检测识别算法研究[J].光电子激光,2015,26(2):320~327 |
| 带钢表面缺陷的一种在线检测识别算法研究 |
| Research on defect surface online detection,classification and recognition algor ithm for strip steel |
| 投稿时间：2014-10-21 |
| DOI： |
| 中文关键词: 带钢表面缺陷 实时检测 特征提取 分类识别 |
| 英文关键词:surface defect of strip real-time detection feature extraction cl assification and recognition |
| 基金项目: |
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| 摘要点击次数: 1265 |
| 全文下载次数: 85 |
| 中文摘要: |
| 为了有效提高在线带钢表面缺陷检测的识别率和 实时性,提出了一种优化的量子粒子群-径向基函数 (QPSO-RBF)网络的带钢缺陷分类识别算法。首先采用加权模糊C-均值聚类(WFCM)算法确定 RBF网络隐含层参 数,算法对带钢缺陷特征数据出现的团状分布与疏散分布问题能够达到很好的聚类划 分,避免对特征数据集等划分的趋势；然后采用QPSO算法对RBF网络的参 数编码成粒子个体,在全局空间中动态地搜索最优适应值的RBF网络参数,提高了网络的 学习性能,并建立了带钢缺陷分类识别的专家知识库。实验结果表明:本文算法可以自动获 得较 优的网络结构,收敛速度快,对带钢缺陷的平均识别率为94.63%,平 均误识率为3.0%,对测试 样本的识别时间为4ms,小于生产线上每张图片的采集周期 10ms,因此,可以为高速生产线上的带钢表面缺陷在线实时检测提供了有利条件。 |
| 英文摘要: |
| In order to improve the detection of surface defects of the strip′s p recision and real-time performance,a new optimized QPSO_RBF network is used in strip defect classifica tion and recognition.Firstly,the parameters of RBF hidden layer are determined by using weighted fuzzy C-means (WFCM) algorithm,cluster distribution and evacuation distribution of the strip feature data can be well handled by WFCM algorithm.The algorithm can avoid feature data set equal partition trend,Then, all network parameters are coded to individual particles in this algorithm,the parameters can dynamicaly search optimal-adaptive values in global space by quantum particle s warms optimization (QPSO),the performance of network learning is improved,and th e strip defect classification and recognition expert knowledge base is established.The experimental results show that the algorithm can obtain more excellent network structure,efficient convergence,th e average recognition rate for strip defects is 94.63%,the average course rate is 3.0%,t he recognition of test sample time is 4ms,and the recognition of test sample time is 10ms less than t he each image collection cycle on production.So the algorithm of this paper can provide favorable co nditions for the scene of the high speed production line of steel strip surface defect real-time detection. |
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