Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.
Currently more and more people concerned about the safety of major public security. Public participant urban infrastructure safety monitoring and investigation has become a trend in the era of big data. In this paper, public participant urban infrastructure safety protection system based on smart phones is proposed. The system makes it possible to public participant disaster data collection, monitoring and emergency evaluation in the field of disaster prevention and mitigation. Function of the system is to monitor the structural acceleration, angle and other vibration information, and extract structural deformation and implement disaster emergency communications based on smartphone without network. The monitoring data is uploaded to the website to create urban safety information database. Then the system supports big data analysis processing, the structure safety assessment and city safety early warning.
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