An operation site security detection method based on point cloud data and improved YOLO algorithm under the architecture of the power internet of things

Abstract

An operation site safety detection method based on point cloud data and improved YOLO algorithm under the power Internet of Things architecture is proposed to address the complex environment of power construction sites and the poor effectiveness of most existing object detection methods. Firstly, an operation site safety supervision system was designed based on the power Internet of Things architecture, and efficient image processing was achieved through cloud edge collaboration. Then, point cloud data and on‐site monitoring information are used on the edge side to extract the accessible area, ensuring that the target is located in a safe area. Finally, the YOLO algorithm is improved in the cloud by using clustering algorithms, network structure optimization, and other methods, and used to detect targets and determine whether their behaviour meets the safety requirements of the operation site. Based on the PyTorch deep learning framework, the proposed method was experimentally demonstrated, and the results showed that its average detection accuracy and time were 94.53% and 68 ms, respectively, providing technical support for achieving remote monitoring of power operation sites.