Automated texture classification of marble shades with real-time PLC neural network implementation
Automated texture classification of marble shades with real-time PLC neural network implementation
复制标题
通过实时 PLC 神经网络实现对大理石色调进行自动纹理分类
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
A. Tzokev
中科院分区：
文献类型：
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作者：
I. Topalova;A. Tzokev
大理石的主观评价，其视觉外观的基础上，可以取代自动纹理分类系统，旨在实现高的分类精度和生产效率。从计算的角度来看，现有的大理石分类方法要么太复杂，要么非常昂贵。如今，大理石行业中的一些自动化质量控制任务和色度分类的检测系统过于昂贵，并且仅与特定的技术设备兼容。本文提出了一种新的大理石砖相似色调分类的方法。它是基于简单的图像预处理，训练的MLP神经网络（MLP NN）与大理石直方图和可编程逻辑控制器（PLC）的实时执行的算法的实施。提出了一种以MLP参数优化和拓扑优化为目标的MLP神经网络训练方法。所设计的自动化系统仅使用标准PLC模块和通信接口。实验测试结果时，识别大理石纹理添加运动模糊的代表和讨论。使用不同的训练集和测试集评估建模技术的性能。分类精度的结果进行比较，通过类似的方法获得的其他结果。
The subjective evaluation of marbles based on their visual appearance could be replaced by an automated texture classification system, intending to achieve high classification accuracy and production effectiveness. The existing marble classification methods from a computational point of view are either too complex or very expensive. Nowadays some inspection systems in marble industry that automates the quality-control tasks and shade classification are too expensive and are compatible only with specific technological equipment. In this paper a new approach for classification of marble tiles with similar shades is proposed. It is based on simple image preprocessing, on training a MLP neural network (MLP NN) with marble histograms and implementation of the algorithm in a Programmable Logic Controller (PLC) for real-time execution. A method for training the MLP NN aiming optimization of MLP parameters and topology is proposed. The designed automated system uses only standard PLC modules and communication interfaces. The experimental test results when recognizing marble textures with added motion blur are represented and discussed. The performance of the modeling technique is assessed with different training and test sets. The classification accuracy results are compared to other results obtained by similar approaches.