Intelligent monitoring system for danger sources of infrastructure construction site based on deep learning
Zhang Limin, Bo Yuan Yang, Chai Pei, Nie Wenzhao, Rui Li · 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · 2020
To master the real-time security status of the infrastructure construction site, an intelligent monitoring system for the recognition of the danger sources at infrastructure construction site, which is based on the theory of depth learning, is proposed. The monitoring system mainly includes signal acquisition equipment, internal optical network, server control center and display terminal. The intelligent identification algorithm of the danger sources is the core of the entire system. The characteristics from the image signal are extracted by the sparse self-coding, which is used to train the neural network. Then, the convolution is proposed to reduce the dimensionality of the features. The experiments confirm that the identification algorithm based on deep learning illustrates high accuracy in hazard identification. In terms of feedback, the identification results will be transmitted to the display terminal, and the safety status of the entire infrastructure construction site can be fully controlled to ensure the safety of both the infrastructure site and the power system.