Authors
Pan Tang, Heyu Xiang, Fangqun Gao, Yuntao Wu, Yanduo Zhang, Tao Lu, Huabin Zhou, Md Zakaria Haque Sakib
Published in
Sensors (Basel, Switzerland). Volume 26. Issue 20. Oct 17, 2026. Epub Oct 17, 2026.
Abstract
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 mAP50-95 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.
PMID:
42944461
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.
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