Is overfeat useful for image-based surface defect classification tasks?

Pei Hung Chen, Shen Shyang Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

35 Scopus citations

Abstract

One of the challenges for real-world image-based surface defect classification task is the lack of labeled training samples to extract useful features to confidently classify defects. In this paper, we present results on our investigation on whether features derived from OverFeat, a variant of Convolution Neural Network, can be used directly for image-based surface defect classification task. We show that the classification performance of two real-world defect images datasets can be significantly different. For the harder classification task, OverFeat features are useful for some types of surface defects, but performs poorly when the defects demonstrate characteristics beyond texture patterns. We propose a simple heuristic approach called Approximate Surface Roughness (ASR) that provides auxiliary information on the relationship between spatial regions in the defect image to be used together with the OverFeat features. Empirical results show improvement in classification performance for those defect types that do not classify well using only OverFeat features.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages749-753
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Externally publishedYes
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period9/25/169/28/16

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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