Jumper Line Detection Method for Situational Awareness of Aerial Lift Operations in Live-Line Maintenance of Overhead Distribution Systems

Joonhyeok Moon¹, Siheon Jeong¹, Byeonghyun Lee¹, Jeik Choi¹, Ki-Yong Oh^1,2,*
1 Department of Mechanical Convergence Engineering, Hanyang University, 222 Wangsimni-ri, Seongdong-gu, Seoul, Republic of Korea
2 School of Mechanical Engineering, Hanyang University, 222 Wangsimni-ri, Seongdong-gu, Seoul, Republic of Korea
* Corresponding Author: Ki-Yong Oh. Email: email
(This article belongs to the Special Issue: Data-Driven and Physics-Informed Machine Learning for Digital Twin, Surrogate Modeling, and Model Discovery, with An Emphasis on Industrial Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.081475

Received 03 March 2026; Accepted 18 May 2026; Published online 15 June 2026

Abstract

Maintaining overhead distribution facilities inherently involves high risks for operators, where ensuring worker safety and operational efficiency remains a paramount challenge. In particular, automating the positioning of aerial work platforms is crucial to mitigate electrocution hazards during live-line maintenance tasks. This paper proposes a novel autonomous framework for detecting jumper lines that could be employed to estimate the optimal bucket position in live-line maintenance of overhead distribution systems. The proposed framework comprises three core modules to form a unified pipeline for autonomous field inspection: a 4D multi-modal map, Sparse-dense fusion network (SDFNet), and Rotational multi-pyramid Transformer with texture and augmentation (RoMP-Tax). The 4D multi-modal map aims to establish an accurate spatial-temporal representation of the maintenance area by integrating light detection and ranging (LiDAR), camera, inertial measurement unit (IMU), and global navigation satellite system (GNSS) measurements. The SDFNet detects telegraph poles from the 4D multi-modal map through geometry, pseudo, and fusion streams, which effectively extract both geometric and optical features. The RoMP-Tax, designed with a hybrid CNN–Transformer architecture enhanced by LBP-based texture encoding and Mixup augmentation, identifies insulators under complex textures and varying illumination. Extensive evaluations on field measurements and benchmark datasets demonstrate the high accuracy and consistent performance of the proposed framework with respect to multiple quantitative metrics, validating its robustness and generalizability. The proposed framework, deploying core technologies of the fourth industrial revolution, provides a reliable and efficient solution for estimating optimal bucket positioning, thereby contributing to the establishment of safe, data-driven live-line maintenance of distribution facilities.