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Fine-Grained Channel Pruning for Deep Residual Neural Networks Read chapter Read first chapter

2020 | OriginalPaper | Chapter

A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection

Authors : Yajie Li, Yiqiang Chen, Yang Gu, Jianquan Ouyang, Jiwei Wang, Ni Zeng

Published in: Artificial Neural Networks and Machine Learning – ICANN 2020

Publisher: Springer International Publishing

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Abstract

Surface defect detection is an indispensable step in the production process. Recent researches based on deep learning have paid primarily attention to improving accuracy. However, it is difficult to apply in real situation, because of huge number of parameters and the strict hardware requirements. In this paper, a lightweight fully convolutional neural network, named LFCSDD, is proposed. The parameters of our model are 11x fewer than baselines at least, and obtain the accuracy of 99.72% and 98.74% on benchmark defect datasets, DAGM 2007 and KolektorSDD, respectively, outperforming all the baselines. In addition, our model can process the images with different sizes, which is verified on the RSDDs with the accuracy of 97.00%.

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previous chapter Fine-Grained Channel Pruning for Deep Residual Neural Networks
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Footnotes
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Metadata
Title
A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection
Authors
Yajie Li
Yiqiang Chen
Yang Gu
Jianquan Ouyang
Jiwei Wang
Ni Zeng
Copyright Year
2020
Book
Artificial Neural Networks and Machine Learning – ICANN 2020

Print ISBN: 978-3-030-61615-1

Electronic ISBN: 978-3-030-61616-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-61616-8_2

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