A Precision Detection Method for Key Components of Power Transmission Towers Oriented to UAV Autonomous Localization

Abstract

To address the challenges of multi-scale differences, complex background interference, and unstable small target positioning in visual inspection of power towers, the existing methods still face issues such as insufficient feature interaction and unstable confidence estimation, which lead to performance degradation in complex backgrounds and occlusion conditions. This paper proposes a precise inspection method for key power tower components using autonomous drone positioning. To this end, this paper makes three key improvements to the you only look once version 11 (YOLOv11) framework. First, it constructs C3k2-adaptive multi-receptive field block (C3k2-AMRB), combining multiple dilated convolutions with a reparameterization mechanism to significantly expand the receptive field and enhance multi-scale feature extraction. Second, it designs a hierarchical wavelet interaction unit (HWIU), which leverages high- and low-frequency decomposition and reconstruction of wavelet transform (WT) to achieve cross-scale semantic alignment, enhancing feature discriminability in complex backgrounds. Third, it proposes a distribution-aware confidence refinement head (DACR-Head), which adaptively calibrates classification confidence based on the statistical characteristics of the predicted bounding-box corner distribution, improving the localization stability and accuracy of small targets. Experiments on the inspection of power line assets dataset (InsPLAD) dataset show that the integrated approach achieves a component detection accuracy at intersection over union (IoU)=0.5 (CDA₅₀) of 88.3% and a component detection robustness (CDR_50:95) of 69.8%, respectively, improvements of 4.4% and 7.0% over the baseline.

Keywords

unmanned aerial vehicle (UAV) autonomous localization / power transmission tower / object detection / wavelet-based feature interaction / confidence calibration