Transfer learning for automated optical inspection

Kim, Seunghyeon; Kim, Wooyoung; Noh, Yung-Kyun; Park, Frank C.

doi:10.1109/ijcnn.2017.7966162

Cited by 101Industrial Vision Systems and Defect DetectionDomain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Transfer learning for automated optical inspection

Kim, Seunghyeon, Kim, Wooyoung, Noh, Yung-Kyun, Park, Frank C.·2017·2017 International Joint Conference on Neural Networks (IJCNN)

DOI 10.1109/ijcnn.2017.7966162

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Abstract

One of the challenges in applying convolutional neural networks to automated optical inspection is the lack of sufficient training data. In this paper we show that transfer learning can be successfully applied using image data from an entirely different domain. Focusing on optical inspection of texture images, we transfer weights from a source network trained with arbitrary unrelated images from the ImageNet dataset. Inspection experiments using our method show that one epoch of fine-tuning is sufficient to achieve 99.95% classification accuracy, while conventional transfer learning without fine-tuning achieves only 78.76%. An in-depth analysis of the effects of fine-tuning reveals that after fine-tuning, most of the unnecessary features encoded in the weights of the source network are deactivated, while meaningful features of the target data are amplified to capture new variations in the target domain.

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Transfer learning can be successfully applied using image data from an entirely different domain.
“Author [21] showed that transfer learning can be successfully applied using image data from an entirely different domain.”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [21]
In surface inspection, a fine-tuned ImageNet pretrained VGG-16 is used as a patch classifier; fine-tuning all layers is the best valid way for models to achieve high accuracy.
“In the practice of surface inspection [15], a fine-tuned ImageNet pretrained VGG-16 is used as a patch classifier on surface defect inspection. The experiment shows that fine-tuning all layers is the best valid way for models to achieve high accuracy.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [15]
VGG-16 gets the best mAcc (99.93%) overall on DAGM.
“Rich features learned from ImageNet push the VGG-16 well-fit to the DAGM and get the best mAcc(99.93%) overall.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [15]
VGG-16 parameters are 134.31 M, causing 9024 M FLOPs in one sub-image, and 9 times FLOPs are needed in an entire input image.
“However, the parameters of a VGG-16 are 134.31 M, which causes very large computational cost—9024 M FLOPs in one sub-image, 9 times FLOPs are needed in an entire input image.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [15]

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Relational-based transfer learning for automatic optical inspection based on domain discrepancy
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Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning
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Authors: Kim, S., Kim, W., Noh, Y.-K., & Park, F. C.
Affiliations: Seoul National University (KR)
ORCID: Kim, Wooyoung
Noh, Yung-Kyun
Park, Frank C.
Venue: 2017 International Joint Conference on Neural Networks (IJCNN)
Publisher: IEEE
Published: 2017-05
Pages: 2517-2524
ISSN: 2161-4407 (ISSN-L)
DOI: 10.1109/ijcnn.2017.7966162
OpenAlex: W2735783528
Cited by: 101
References: 19
Citations by year: 2026: 1 · 2025: 7 · 2024: 13 · 2023: 18 · 2022: 23 · 2021: 13 · 2020: 12 · 2019: 10 · 2018: 4
FWCI: 10.44
Type: conference paper
Language: en
Open access: closed
Locations: International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
Topics: Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: transfer learning, automated optical inspection, fine-tuning, convolutional neural networks, ImageNet, texture images, limited training data, domain shift

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Transfer learning for automated optical inspection

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