Analyzing Deep-Learning Methods for Power Line Component Detection in Unmanned Aircraft System Imagery With Few Data

Due to the critical role of power lines in modern infrastructure, numerous automated methods have been developed for their inspection. Among these, Unmanned Aircraft Systems (UAS) have emerged as a valuable tool, offering rapid and precise inspections by capturing high-resolution aerial imagery of power lines. Drones enable access to hard-to-reach areas, reduce safety risks for workers near live wires, and significantly lower the time and cost associated with traditional inspection methods. In particular, deep learning techniques have been widely applied to automate the analysis of key components via the onboard camera. However, these methods typically rely on a first stage of detection based on large, annotated datasets focused on specific components, limiting their adaptability to new or unseen components. This paper investigates the application of two state-of-the-art algorithms of Few-Shot Object Detection (FSOD) for power line component detection: DeFRCN and CD-ViTO, alongside a modified Yolov8 detector of our own in which we integrated the modules of DeFRCN. We evaluate their performance using both public and proprietary datasets, analyzing unexpected outcomes and provide insights into the practical applicability of FSOD in real-world scenarios.