基于改进差分盒算法的织物疵点自动检测方法
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(东莞职业技术学院 计算机工程系,广东 东莞 523808)

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曹文梁(1980-),男,江西泰和人,硕士,主要从事算法设计与分析、网络安全等研究。[FQ)]

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TP751

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国家自然科学基金项目(70971043)。


A Method of Detection of Fabric Defect Based on Improved Differential Boxing-counting Algorithm
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(Department of Computer Engineering, Dongguan Polytechnic, Dongguan 523808,China)

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    摘要:

    根据织物检测的实际情况需要,提出了基于差分盒算法的改进算法,在使用Brodatz纹理库样本的前提下,分别在盒子高度确定、盒子总数统计以及网格中盒子数量确定3个方面进行改进,在算法时间和精度两项上,对经典差分盒算法和改进算法进行了比较,对比得出了改进算法的时效性;还应用了改进差分盒算法对3种常见纹理的疵点织物进行了计算,以确认疵点织物,并验证了改进差分盒算法;实验通过检出率、误检率、漏检率和检测精度4个检测精度参数表明,改进的差分盒算法可以有效地区分疵点织物和正常织物,该方法具有很强的实用性。

    Abstract:

    For the need of detection of fabric defects, the paper presents an improved algorithm based on differential box-counting (IDBC) method. The algorithm modifies the highness of the boxes, the method of determining the amount of boxes in grids and the statistics of calculating the total of all the boxes on the premise of using Brodatz texture library sample. On the two aspects of the time and accuracy, the paper compared the new algorithm with classical differential box-counting method(DBC) and obtained the efficiency of the improved algorithm. At the same time, the paper calculated defect fabric of the three common texture employing improved differential box-counting method to verify defect fabric. Through four parameters, i.e. detection rate, false drop rate, loss rate and detection precision, the results of experiments demonstrate this new method is effective and can outperform the well-known differential boxing-counting (DBC) method.

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曹文梁.基于改进差分盒算法的织物疵点自动检测方法计算机测量与控制[J].,2014,22(6):1676-1679.

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  • 收稿日期:2014-01-12
  • 最后修改日期:2014-03-05
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  • 在线发布日期: 2014-11-12
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