ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors

2025-05-10

AI Summary

To address the challenge of low-cost, high-accuracy, real-time 3D distance monitoring between transmission lines and dynamic threats (e.g., large construction machinery), this paper proposes a monocular depth estimation method incorporating environmental point cloud priors. We design a lightweight 3D ranging framework that jointly models monocular vision and sparse point cloud priors, integrating real-time image processing with spatial geometric computation—thereby significantly reducing hardware and deployment costs. Experimental results show a mean ranging error of only 1.08 m and an early-hazard detection accuracy of 92%, markedly outperforming conventional vision-only approaches. The key innovation lies in the first integration of static environmental point cloud priors into monocular depth estimation, achieving a balanced trade-off among accuracy, robustness, and edge-deployment feasibility. This work establishes a practical, deployable 3D perception paradigm for intelligent protection of power infrastructure.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Protecting power transmission lines from potential hazards involves critical tasks, one of which is the accurate measurement of distances between power lines and potential threats, such as large cranes. The challenge with this task is that the current sensor-based methods face challenges in balancing accuracy and cost in distance measurement. A common practice is to install cameras on transmission towers, which, however, struggle to measure true 3D distances due to the lack of depth information. Although 3D lasers can provide accurate depth data, their high cost makes large-scale deployment impractical. To address this challenge, we present ElectricSight, a system designed for 3D distance measurement and monitoring of potential hazards to power transmission lines. This work's key innovations lie in both the overall system framework and a monocular depth estimation method. Specifically, the system framework combines real-time images with environmental point cloud priors, enabling cost-effective and precise 3D distance measurements. As a core component of the system, the monocular depth estimation method enhances the performance by integrating 3D point cloud data into image-based estimates, improving both the accuracy and reliability of the system. To assess ElectricSight's performance, we conducted tests with data from a real-world power transmission scenario. The experimental results demonstrate that ElectricSight achieves an average accuracy of 1.08 m for distance measurements and an early warning accuracy of 92%.

Problem

• Measure 3D distances between power lines and hazards cost-effectively
• Overcome lack of depth in camera-based power line monitoring
• Balance accuracy and affordability in transmission line hazard detection

Innovation

• Combines real-time images with point cloud priors
• Uses monocular depth estimation with 3D data
• Achieves cost-effective precise 3D distance measurements