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多级值与分歧测量以及改进的粒子群优化算法,用于裂纹图像细分.

Fangyan Nie1, Mengzhu Liu2, Pingfeng Zhang3

  • 1Computer and Information Engineering College, Guizhou University of Commerce, Guiyang, 550014, China. niefyan@vip.163.com.

Scientific reports
|April 1, 2024
PubMed
概括

本研究引入了一种新的多级值方法,用于使用最小算术-几何分歧进行裂纹图像细分. 增强的粒子群优化算法提高了复杂工程结构的检测精度和效率.

关键词:
裂纹检测 裂纹检测 裂纹检测 裂纹检测局部随机扰动局部随机扰动最小的算术-几何差异.多层次图像的值.粒子群集优化优化 粒子群集优化

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算机科学 计算机科学
  • 图像处理 图像处理

背景情况:

  • 在工程结构中形成裂会带来重大安全风险.
  • 及时检测裂对于结构完整性至关重要.
  • 图像值细分是用于裂识别的关键机器视觉技术.

研究的目的:

  • 为裂纹图像细分提出一种新的多级值方法.
  • 在复杂场景中提高裂纹检测准确度.
  • 提高多级值算法的效率.

主要方法:

  • 一种基于裂纹图像细分的最小算术-几何分歧的多级值方法.
  • 一个增强的粒子群优化算法与局部随机扰动来解决时间复杂性.
  • 使用像素值分布特征进行自适应值的确定.

主要成果:

  • 与七种最先进的技术相比,提出的方法显示出更高的性能.
  • 包括RMSE,PSNR,SSIM和FSIM在内的定量指标显示出显著的改善.
  • 增强的粒子群优化算法改善了多样性和全球趋同.

结论:

  • 建议的最小算术-几何分歧多级值方法对于裂纹图像分割是有效的.
  • 增强的粒子群优化算法提供了更高的效率和准确性.
  • 这种方法为检测复杂工程结构中的裂提供了强大的解决方案.