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.
- Aerospace Division
Real-Time Detection of Ancient Architecture Features Based on Smartphones
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Zou, Z, Wang, N, Zhao, P, & Zhao, X. "Real-Time Detection of Ancient Architecture Features Based on Smartphones." Proceedings of the 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. V002T05A015. ASME. https://doi.org/10.1115/SMASIS2018-8265
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