Real-Time Early Safety Warning for Personnel Intrusion Behavior on Construction Sites Using a CNN Model

Abstract

The high number of annual safety accidents and casualties reflects the problems of slow detection of safety accidents and untimely early warnings in current construction safety management, and China urgently needs new methods and technologies to improve the safety management efficiency of the construction industry. However, there are fewer achievements in the use of new technologies for intelligent construction safety management, and most of the research focuses on intrusion detection and specific event alarms, which cannot be well implemented for systematic early warning functions. Based on the existing research and the characteristics of early warning scenarios, this study introduces the convolutional neural network (CNN) to build a video image recognition and classification model to give early safety warnings for intrusion behavior in hazard areas of construction and demonstrates the warning effect and accuracy with practical cases. First, it clarifies the early warning demand information, such as the attributes of construction personnel and hazard areas. Then, the construction model is realized by multi-scale hierarchical feature extraction mapping, the Softmax classification function, and the argmax function. Finally, from the empirical analysis, it can be seen that an early safety warning based on the CNN model has an accurate ability to identify the intrusion behavior of construction site personnel, which can reduce the probability of construction safety accidents to a certain extent, and provide enlightenment for further realization of intelligent construction sites.