Deep learning-based substation remote construction management and AI automatic violation detection system

Abstract

Workplace video surveillance and timely response to operational violations are critical to avoid operator injuries at power construction sites. Here, a system that combines remote substation construction management and artificial intelligence object detection techniques to intellectualize the power construction management process and identify violations during construction in real time is proposed. To improve the detection accuracy, a data augmentation method, including three operations: (1) object segmentation and background fusion; (2) partial erasing; and (3) other basic transformations, is also proposed. Six variants of the You Only Look Once (YOLO) model are trained as detectors for comparative experiments on a dataset collected at the practical construction site. The experimental results show that the detection precision and recall of the YOLOv5-s model are 0.852 and 0.922, with high accuracy and low miss rate, which meet the requirements of robustness and accuracy in detecting realistic power construction violations.