An Approach Based on LiDAR and Spherical Images for Automated Vegetation Inspection in Urban Power Distribution Lines

Abstract

Preventive tree pruning is an efficient way to reduce the risks of outages in the electrical network, such as cable rupture, short circuits, and fires, which may occur due to interferences between vegetation and urban electrical distribution lines. These interferences are listed as one of the main causes of power supply interruptions by distribution companies and, consequently, generate significant financial losses due to the decline in quality indicators and the need for maintenance. Vegetation management inspection is essential to maintain the reliability and continuity of the power distribution, but manual monitoring is expensive, time-consuming, and subjective. This paper proposes a new method based on Deep Learning (DL) and high-resolution sensors for automated vegetation inspection in urban power distribution lines. The method combines Light Detection and Ranging (LiDAR) and spherical images and overcomes the limitations of aerial images using vehicle-borne sensors. The dataset was obtained using the proposed capture system in four Brazilian cities. The results demonstrated the suitability of the proposed method in an innovative way through a proof of concept. Among the main contributions, the functional evaluation of the system for Artificial Intelligence-based real-time vegetation inspection in power line distribution is highlighted. The proposed method achieved high rates of interference identification using a web platform and mobile application (achieving a precision of over 94%). The results demonstrate the potential of the proposed approach for automated vegetation inspection in real-world scenarios and open up opportunities for the deployment and evaluation of large-scale applications.

Keywords

• Artificial intelligence
• deep learning
• interference detection
• LiDAR
• power distribution
• preventive pruning