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Using deep learning methods to recognize and measure silica spheres in SEM pictures.

License: MIT License

electron-microscopy-datasets scanning-electron-microscope chemistry electron-microscopy nanoparticles

dlsilicaspheres's Introduction

DLSilicaSpheres

Using deep learning methods to recognize and measure silica spheres in SEM pictures.

dlsilicaspheres's People

Contributors

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Watchers

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dlsilicaspheres's Issues

Dataset annotation

基础信息

CVAT平台: https://app.cvat.ai
任务链接: https://app.cvat.ai/tasks/103537

工作流程

  • 粗标注,覆盖所有小球即可,要求检查对象是否重复/漏标
  • 精修,明确区分小球和背景,要求检查边缘是否准确(或许结合bitmap)
  • 邻接分割,确定小球交界处像素点归属,要求检查对象覆盖不重不漏
  • 导出使用,验证数据集正确显示+元数据良好+框架识读正常
  • 最终版将元数据写在配置文件中,并修改图片文件编号发布

标注标准

  • 在原图下标出灰-黑界限围出的小球,不使用直方图均衡(需要回顾电镜成像过程)
  • 被遮盖小球,若能识别出半径和圆心,将可见部分标记上,包括上方小球投射下来的阴影

Tips

  • 使用美国的节点
  • 手动修改:选中对象后 shift + 双击
  • 周期性强的图片善用复制粘贴
  • HRNet 对图片边缘截断的小球识别效果较好

疑问

  • 不完整小球是否考虑另外起一类?不然会污染正常小球数据?

Proposal

4.14, 5 min PPT

  • 确认是否需要写开题文书?400~500 words

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