A Transformer-Based Framework with Multi-Scale Feature Reconstruction for UAV Power Inspection

Accurate detection of transmission line components is crucial for the stability and security of power grid operations. However, accurate power line inspection is always affected by complex factors, such as multi-scale objects, complex background interference, and object occlusion, etc. To tackle these complexities, this paper leverages the long-range dependency modeling advantages of the Transformer architecture, and an improved real-time end-to-end Detection Transformer (RT-DETR) with multi-scale feature reconstruction, referred to as MFRRT-DETR, for Unmanned Aerial Vehicle (UAV) inspection systems is presented. Specifically, an enhanced attention-based backbone network integrated via an aggregated pixel-focus attention (APFA) module is built which uses a dual-path design with fine-grained and coarse-grained branches to combine pixel-level focus with global perception to enhance the interaction between local and global features, alleviating the limitations of the local receptive field in Convolutional Neural Networks (CNNs). To further overcome the issues of target overlap, occlusion, and foreground–background confusion, a context-guided spatial feature reconstruction feature pyramid network (CGR-FPN) module is proposed which strengthens foreground representation and effectively fuses multi-scale features, improving performance in crowded scenes. Additionally, a Focaler–Shape IoU loss function is introduced to mitigate class imbalance issues and localization errors by focusing on hard samples and optimizing bounding box regression, particularly for long and wide irregular rectangular targets. Experiments show that the proposed MFRRT-DETR significantly outperforms advanced detection models, which effectively validates the detection efficiency and accuracy of the proposed model in complex inspection scenarios, making it a promising solution for UAV-based power line inspection.