论文阅读笔记《Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networ》
核心思想 本文提出一种能够对缺陷进行像素级分割和分类的算法，整个网络可以分成两个阶段：缺陷检测阶段和缺陷分类阶段。在缺陷检测阶段，作者设计了一个级联的自动编码器（CASAE），利用编码器对正常区域和缺陷区域的响应不同，通过阈值分割的方式实现缺陷区域的像素级定位。然后把缺陷区域裁剪出来得到大小不一的图块，将图块输入到缺陷分类网络，最终输出缺陷的类别。整个算法的流程如下图所示 如图所示CASA
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