Vision-Based Multi-Wire Detection and Tracking for UAV Wire Approach

Abstract

Reliable detection and tracking of power lines is critical for enabling
under-wire UAV approach and inductive power-line charging to extend
UAV range. However, wires are thin, featureless, and visually ambiguous
structures that challenge traditional computer vision methods and degrade
depth estimation accuracy. To address these challenges, this thesis presents
a fully passive, camera-only multi-wire detection and tracking algorithm
that operates in real time on lightweight onboard compute using only
stereo RGB imagery.

The proposed framework integrates a lightweight classical vision pipeline
with three-dimensional geometric reasoning and a multi-stage filtering
process to produce robust wire instance detections. A complementary
oriented object detection model is trained using labels generated by the
classical pipeline, leveraging its fine-tuned geometric outputs to improve re-
silience in challenging visual conditions. To track individual wire instances
across frames, we introduce a Kalman-filter-based tracking architecture
that estimates both wire orientation and per-wire positional state while
remaining robust to wire detection outliers and vehicle pose drift. The
system is further expanded by testing a wire positional servoing approach
using the tracked wire instances in simulation and is validated across a
diverse range of data sources, including simulation, indoor testing, and
outdoor flight evaluations.