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 resilience 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, handheld experiments, and outdoor flight evaluations.