Evaluation of Data Augmentation on Surface Defect Detection

Isack Farady, Chih Yang Lin, Fityanul Akhyar, R. Roshini, John Sahaya Rani Alex

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper, we have investigated and benchmarked the augmentation approach of image augmentation to increase or provide a different result in detection accuracy compared to the basic method without augmentation. We explored two different methods of pixel-wise operations: pixel domain manipulation and spatial domain transformation to analyze the effect of increasing data for typical defect detection problems. We used two object detection models Faster R-CNN and Cascade R-CNN on top of ResNet-50 as our baseline models. To gain accuracy, we found that the effectiveness of data augmentation for defect detection is influenced by network complexity and the surface defect properties.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433280
DOIs
StatePublished - 2021
Event8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, Taiwan
Duration: 15 Sep 202117 Sep 2021

Publication series

Name2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021

Conference

Conference8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Country/TerritoryTaiwan
CityPenghu
Period15/09/2117/09/21

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