Safe Distance Monitoring of Live Equipment Based Upon Instance Segmentation and Pseudo-LiDAR

Abstract

Electrocution accidents caused by operation and maintenance personnel and high-voltage live equipment frequently occur in substations. Although many cameras have been installed for the surveillance of critical energized equipment, they cannot complete the monitoring of safe distance. In addition, the current research based on vision or LiDAR suffers from computationally intensive, lack of real-time or expensive equipment. In order to meet the low cost, easy maintenance and real-time requirements of substation safe distance monitoring, a monocular vision method based on 2D-3D fusion is proposed in this study. Specifically, instance segmentation, depth estimation, depth reconstruction, and back projection transformation are used to predict the 3D distance of objects in 2D images. The work is mainly implemented in two aspects: a) To obtain the high-quality masks of Power Transformers and Person, SOLOv2 is optimized on three aspects: feature extraction, feature fusion and ability to cope with object geometric transformation. b) Pseudo-LiDAR output from RGB images via depth estimation and back projection techniques. Subsequently, combined with the point cloud of the object and the actual-to-virtual conversion ratio, the actual distance between Power Transformer and Person can be calculated. Experimental results show that the maximum error of ranging is within 9%, with an average error rate of 5.34% on the Power Transformer-Person dataset. It can be seen that the proposed method has achieved good ranging effects, and can realize the automatic measurement of the safe distance of Power Transformer.