UAV-Based Transmission Tower Inspection Using Hierarchical Multitask Learning Under Heterogeneous Supervision

Abstract

Unmanned aerial vehicle (UAV) inspection of transmission towers requires joint analysis of corridor geometry, tower components, and localized defects, whereas available datasets provide incompatible annotations at different scales. Tower-HMT is a hierarchical multitask network that shares a ConvNeXt-Tiny encoder and feature pyramid across corridor parsing, component parsing, missing-bolt localization, and component-condition recognition. Four public real-image datasets are organized by native supervision rather than merged into a flat label space. Task-conditioned feature modulation separates dataset statistics; topology and boundary losses preserve thin conductors and lattice edges; and a detached tower-probability gate supplies structural context to a high-resolution bolt head. Source-image groups define non-overlapping training, validation, and test partitions. On held-out data, the network achieved foreground mIoU values of 0.597 for tower-conductor parsing and 0.561 for box-conditioned component parsing, an AP50 of 0.123 for missing-bolt localization, and a macro-F1 of 0.925 for component-condition recognition. Boundary, class-wise, robustness, threshold-sensitivity, and latency results quantify the effects and limitations of hierarchical learning. The framework provides a reproducible real-image baseline for review-oriented inspection, while low missing-bolt localization accuracy and weak component masks remain the principal constraints.