Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

Abstract

Aiming at the problems of low recognition accuracy and large memory occupation when using point cloud information for power operation violation, A power operation violation recognition method based on point cloud data preprocessing and deep learning under the architecture of Internet of things (IoT) is proposed. First, voxel filtering and statistical filtering methods are used to properly simplify the power operation point cloud data on the premise of ensuring the quality of reverse modeling, and the moving least square method is used to smooth the point cloud to obtain a complete and closed three-dimensional model; second, the process of power operation violation behavior recognition is divided into two stages. In the first stage, PointRCNN extracts the semantic features of each point, separates the front scenic spots, and extracts the preselection box. In the second stage, the candidate box is refined by integrating the semantic features and classification confidence of the first stage to obtain a more accurate bounding box. Finally, the experiments show that the average accuracy of the proposed method is the highest, with an average accuracy of 0.919 in the simple difficulty scenario, 0.897 in the medium difficulty scenario, and 0.839 in the difficult difficulty scenario, which are higher than those of the compared methods. Therefore, the proposed method can effectively improve the accuracy of power operation violation identification.