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

Ruoxu Ren, Terence Hung, Kay Chen Tan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

386 Citations (Scopus)

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.

Original languageEnglish
Article number7864335
Pages (from-to)929-940
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume48
Issue number3
DOIs
Publication statusPublished - Mar 2018
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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