Deep Learning Based AOI System with Equivalent Convolutional Layers Transformed from Fully Connected Layers

Yu-Hsuan Tsai, N. Y. Lyu, S. Y. Jung, K. H. Chang, J. Y. Chang, Chuen-Tsai Sun

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

1 Scopus citations

Abstract

The rise of deep learning, especially in the realm of computer vision, paves ways of leveraging automatic optical inspection systems to a higher level. Convolutional neural networks and its derivatives might be the most widely used architectures for defect inspection tasks. In real cases the amount of collected data is often not large, so transferring learning and data augmentation are necessary. In this paper, we explain some details how we implement the deep learning based AOI system where fully connected layers are replaced by convolutional layers, then a classification heat map is output after post-processing. We examine the performance of our model with two data sets collected in industrial manufacturing cases. We further propose an idea to transfer models pretrained on augmented data of different sizes cropped from original image to the present classification task for possible improvements of the performance.
Original languageEnglish
Title of host publication2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)
Place of PublicationNEW YORK
PublisherIEEE
Pages103-107
Number of pages5
ISBN (Print)978-1-7281-2493-3
DOIs
StatePublished - 17 Oct 2019
Event2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 - Hong Kong, China
Duration: 8 Jul 201912 Jul 2019

Conference

Conference2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
CountryChina
CityHong Kong
Period8/07/1912/07/19

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