A Generic Deep-Learning-Based Approach for Automated Surface Inspection

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

177 Scopus Citations
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Detail(s)

Original languageEnglish
Article number7864335
Pages (from-to)929-940
Journal / PublicationIEEE Transactions on Cybernetics
Volume48
Issue number3
Online published24 Feb 2017
Publication statusPublished - Mar 2018

Abstract

Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%-25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%-19.00% in three defect types and improves accuracies by 2.29%-9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

Research Area(s)

  • Automated surface inspection (ASI), deep learning (DL), feature transferring, segmentation