UAV-Based Safety-Oriented Object Detection for Live-Line Operations via Multi-Scale, Frequency-Aware, and State-Space Feature Enhancement

Reliable visual sensing is important for UAV-assisted monitoring of live-line power operations, where workers, personal protective equipment (PPE), temporary insulation, and electrical structures appear under large-scale variation, occlusion, and background clutter. This study develops a safety-oriented detector that assigns different feature transformations to different stages of the detection hierarchy. Multi-Scale Selective Fusion (MSSF) combines 3-by-3, 5-by-5, and 7-by-7 receptive-field responses through channel-wise branch weighting. Frequency-Aware Adaptive Fusion (FreqAFU) refines the real and imaginary Fourier components and fuses the reconstructed response with the spatial feature through learned channel gates. BiNet and Mamba-based blocks provide bidirectional cross-scale interaction and state-space contextual modelling. The self-collected corpus contains 8124 annotated images and 118,119 object instances across eight categories, with 6499 training images and 1625 validation images separated at the acquisition-session level. On this fixed protocol, the complete YOLOv8n-based configuration improves mAP_50-95 from 0.558 to 0.635 and mAP₅₀ from 0.900 to 0.949. Ablations, repeated training, scale- and class-level analyses, runtime profiling, and VisDrone evaluation show that the benefit of individual components is configuration dependent rather than universally additive. The detector is positioned as an object-level perception layer; final safety-compliance reasoning remains outside its scope.