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steeldefectdetection's Introduction

SteelDefectDetection

钢材缺陷检测

环境配置

Python3安装

Windows系统下,需要到https://www.python.org/downloads/ 下载安装包进行安装。

项目配置

首先使用git clone项目到本地

git clone https://github.com/zhuohoudeputao/SteelDefectDetection.git

此时执行上述语句的目录会新增一个SteelDefectDetection目录,cd进入该目录,安装依赖项

python3 -m pip install -U -r requirements.txt

然后就可以愉快地开始浏览代码和开发工作了。

数据

从数据来说,我们一开始获得的数据应该是.his文件或者.dicom文件,转换后得到的应该是1024x1024大小的钢管焊缝图,因此数据预处理部分需要的任务是

  • 格式转换,包括.his和.dcm两种格式文件转换为jpg文件

    • his文件格式同dcm文件,都是DICOM标准的文件,使用Pydicom处理即可
    • dcm文件格式已经可以做初步处理,但是仍然有部分看起来是黑色的,不知道为啥会有这种情况
  • 图像裁剪

    • 现有大部分图像是1024x1024的,且钢材焊缝在中间位置
    • 部分图像的大小并不是完全是方形
  • 数据标签

    • 从数据当中提取相应标签与数据关联

单幅钢材图片处理

  • 焊缝区域提取部分算法,在图片不是严格地左右两边较亮,焊缝区域较暗的情况下会失效
  • 增强对比度解决了图片是否要转换颜色的问题,但是似乎凸显出了焊缝边界的方差,导致焊缝边界方差比出现缺陷点的列的方差还要大,得找一个更好的方法来检测缺陷

文献调研

引用 主要内容
[1]李金燕,李春祥,王锡岭.焊缝缺陷图像特征提取的研究[J].焊接技术,2018,47(11):78-82+6.DOI:10.13846/j.cnki.cn12-1070/tg.2018.11.023. 通过平滑滤波预和模糊增强预处理图像,通过阈值法分割背景和主要目标,再采用8连通区域判别法进行缺陷分割,最终计算缺陷的特征用于分类

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