Proceedings of the 2021 International conference on Smart Technologies and Systems for Internet of Things (STS-IOT 2021)

IPPR of Traditional Wooden Building Section Based on Deep Learning

Authors
Xiaodan Liang1, Haoming Dong2, *
1Shanghai Urban Construction Vocational College, Shanghai, China
2Shanghai Jianke Architectural Design Institute CO. Ltd., Shanghai, China
*Corresponding author. E-mail: donghaoming76@163.com
Corresponding Author
Haoming Dong
Available Online 2 June 2022.
DOI
https://doi.org/10.2991/ahis.k.220601.030How to use a DOI?
Keywords
Deep Learning (DL); Traditional Wooden Architecture; Building Section; Image Recognition
Abstract

In recent years, deep learning (DL) and neural network models have become hot topics in new research directions in the field of machine learning and artificial intelligence. In order to protect our traditional wooden buildings, this paper applies the research of DL in IPPR to traditional wooden building profiles in China through the research of a series of IPPR algorithms such as SSD, SVM, DBN, in several different network training environments such as VGG, DenseNet, and ZF, Research on the IPPR accuracy and recognition speed of traditional wooden building sections. The results show that the SSD algorithm has the highest efficiency when the VGG network training environment and IPPR test cases are about 400, which is higher than before the improvement. The algorithm improves the accuracy by 5-10%, and the recognition speed is also increased by 2-3%.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license.

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Volume Title
Proceedings of the 2021 International conference on Smart Technologies and Systems for Internet of Things (STS-IOT 2021)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
2 June 2022
ISBN
978-94-6239-581-7
ISSN
2589-4919
DOI
https://doi.org/10.2991/ahis.k.220601.030How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license.

Cite this article

TY  - CONF
AU  - Xiaodan Liang
AU  - Haoming Dong
PY  - 2022
DA  - 2022/06/02
TI  - IPPR of Traditional Wooden Building Section Based on Deep Learning
BT  - Proceedings of the 2021 International conference on Smart Technologies and Systems for Internet of Things (STS-IOT 2021)
PB  - Atlantis Press
SP  - 153
EP  - 159
SN  - 2589-4919
UR  - https://doi.org/10.2991/ahis.k.220601.030
DO  - https://doi.org/10.2991/ahis.k.220601.030
ID  - Liang2022
ER  -