No abstract

No abstract

No abstract

The complex environment of power construction sites is prone to frequent accidents and many dangerous areas. Using advanced artificial intelligence technology to monitor these dangerous areas in real-time is the primary measure to improve construction safety. Given the problems of poor real-time monitoring and the low accuracy of traditional methods, this paper proposes a real-time monitoring method for dangerous areas based on YOLOv5 deep learning, and through the trained YOLOv5, the construction site is monitored in real-time. Timely warnings are issued for those entering dangerous area to avoid accidents. Verified by the actual data, the method is timely and effective with high identification accuracy.

No abstract