The increasing popularity of civilian unmanned aerial vehicles (UAVs) poses serious safety challenges to low-altitude airspace near power transmission and distribution lines. Existing monitoring technologies rely heavily on expensive radar or radio frequency devices that are susceptible to interference, while mainstream vision schemes are typically limited to object detection and fail to analyze flight intentions. To address these problems, this paper proposes a UAV maneuver recognition method for transmission and distribution line protection, providing precise semantic support for action-based risk warnings through multi-dimensional visual feature fusion. First, a YOLO object detection model extracts real-time UAV bounding boxes and trajectories from video streams to construct raw motion state sequences. Second, to suppress detection noise while preserving critical maneuvering features, a feature-preserving trajectory reconstruction algorithm based on median absolute deviation (MAD) enhancement (FP-SGM) is proposed, which combines a Savitzky-Golay (S-G) filter with an adaptive statistical threshold. On this basis, a multi-dimensional feature space comprising trajectory and scale features is constructed to design a cascade decision strategy. By fusing these features, the lateral and radial motions of UAVs are effectively decoupled, achieving robust recognition of four typical maneuvers: ‘Hovering’, ‘Circling’, ‘Approaching’, and ‘Receding’. Simulation experiments on the Gazebo platform demonstrate a 95% accuracy rate in maneuver recognition. The results indicate that this low-cost method presents a viable potential for integration into existing surveillance terminals, laying a foundation for intelligent power line defense systems.

Consumer drones are widely used for recreational aerial photography, while cross-border operations face inconsistent regulations, fragmented official information, and changing restricted-airspace requirements. This paper proposes LUCA, a localized regulatory-aware intelligent assistance framework based on Large Language Models (LLMs). LUCA acquires regulatory and airspace information, constructs a multidimensional structured Retrieval-Augmented Generation (RAG) knowledge base, and transforms natural-language provisions into computable conditions through a constrained reasoning loop for pre-flight compliance review. On a locked benchmark of 1000 scenarios drawn from United States FAA, Chinese civil-aviation, and EASA regulatory sources, each configuration was independently run five times with the same prompts and retrieval snapshot. LUCA-Qwen3-8B achieved a five-run mean Accuracy of 95.4%; the prespecified primary run classified 954/1000 cases correctly, with 37 false positives and 9 false negatives. The framework supports traceable pre-flight review, but its output is advisory and does not constitute a legal determination or official flight authorization or official flight authorization.