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Real-Time Detection of Ancient Architecture Features Based on Smartphones

[+] Author Affiliations
Zheng Zou, Niannian Wang, Xuefeng Zhao

Dalian University of Technology, Dalian, China

Peng Zhao

Palace Museum, Beijing, China

Paper No. SMASIS2018-8265, pp. V002T05A015; 5 pages
  • ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies
  • San Antonio, Texas, USA, September 10–12, 2018
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5195-1
  • Copyright © 2018 by ASME


Due to the particularity of texture features in ancient buildings, which refers to the fact that these features have a high historical and artistic value, it is of great significance to identify and count them. However, the complexity and large number of textures are a big challenge for the artificial identification statistics. In order to overcome these challenges, this paper proposes an approach that uses smartphones to achieve a real-time detection of ancient buildings’ features. The training process is based on SSD-Mobilenet, which is a kind of Convolutional Neural Network (CNN). The results show that this method shows well performance in reality and can indeed detect different ancient building features in real time.

Copyright © 2018 by ASME



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