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

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

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.
原文English
主出版物標題2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)
出版地NEW YORK
發行者IEEE
頁面103-107
頁數5
ISBN(列印)978-1-7281-2493-3
DOIs
出版狀態Published - 17 10月 2019
事件2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 - Hong Kong, China
持續時間: 8 7月 201912 7月 2019

Conference

Conference2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
國家/地區China
城市Hong Kong
期間8/07/1912/07/19

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