Extremal Region Analysis based Deep Learning Framework for Detecting Defects

Abstract

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.