论文标题
极端地区分析基于检测缺陷的深度学习框架
Extremal Region Analysis based Deep Learning Framework for Detecting Defects
论文作者
论文摘要
本文提出了基于最大稳定的极端区域（MSER）分析，用于基于统一缺陷检测框架的卷积神经网络（CNN）。我们提出的框架利用MSER的一般性和稳定性来生成所需的缺陷候选者。然后，在缺陷候选物上采用了特定的训练有素的CNN分类器，以产生最终的缺陷集。实验中，不同类别的缺陷数据集\ blue {使用}。更一般而言，可以调整MSER中的参数设置以满足各种行业的不同要求（高精度，高召回等）。广泛的实验结果表明了所提出的框架的功效。
A maximally stable extreme region (MSER) analysis based convolutional neural network (CNN) for unified defect detection framework is proposed in this paper. Our proposed framework utilizes the generality and stability of MSER to generate the desired defect candidates. Then a specific trained binary CNN classifier is adopted over the defect candidates to produce the final defect set. Defect datasets over different categories \blue{are used} in the experiments. More generally, the parameter settings in MSER can be adjusted to satisfy different requirements in various industries (high precision, high recall, etc). Extensive experimental results have shown the efficacy of the proposed framework.
