• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Surface Science >A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
【24h】

A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects

机译：基于完整局部二值模式的热轧带钢表面缺陷噪声稳健方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic recognition method for hot-rolled steel strip surface defects is important to the steel surface inspection system. In order to improve the recognition rate, a new, simple, yet robust feature descriptor against noise named the adjacent evaluation completed local binary patterns (AECLBPs) is proposed for defect recognition. In the proposed approach, an adjacent evaluation window which is around the neighbor is constructed to modify the threshold scheme of the completed local binary pattern (CLBP). Experimental results demonstrate that the proposed approach presents the performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes. Even in the toughest situation with additive Gaussian noise, the AECLBP can still achieve the moderate recognition accuracy. In addition, the strategy of using adjacent evaluation window can also be used in other methods of local binary pattern (LBP) variants.
机译：热轧带钢表面缺陷的自动识别方法对钢板表面检测系统很重要。为了提高识别率，提出了一种新的，简单而又健壮的抗噪声特征描述符，称为相邻评估完成局部二进制模式（AECLBP），用于缺陷识别。在提出的方法中，构造了一个在邻居周围的相邻评估窗口，以修改完成的本地二进制模式（CLBP）的阈值方案。实验结果表明，该方法在类别变化，照度和灰度变化的特征变化影响下，表现出缺陷识别的性能。即使在最艰难的情况下，加性高斯噪声，AECLBP仍可以达到中等识别精度。此外，使用相邻评估窗口的策略也可以在其他本地二进制模式（LBP）变体方法中使用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号