论文阅读笔记《Supervised machine learning based surface inspection by synthetizing artificial defects》
核心思想 本文提出一种人工合成缺陷的方法，可用于表面缺陷检测的数据集扩充。为了解决缺陷检测领域中，有缺陷样本数量较少，无法对深度学习网络进行充分训练的问题，本文提出了一种人工合成缺陷的方法。整个合成流程主要包含四个步骤：1.生成骨架，2.纹理生成，3.对无缺陷图像的修改，4.缺陷可视化程度的分析。 首先，采用一种随机游走的方式来生成一个二维的缺陷图像骨架或者说轮廓，从图中的一个随机坐标点出
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