Abstract: Deep learning-based foreign object detection techniques for power transmission lines have become a critical enabler for the stable operation of next-generation power grids. To further enhance detection performance for foreign objects on power transmission lines, a hypergraph-based position-aware and global-aware model was proposed, which builds upon Hyper-YOLO’s ability to model and propagate high-order semantic correlations across levels and positions. Firstly, a position-aware multi-branch feature aggregation module (PMFA) was introduced to facilitate precise identification of critical feature representations. Additionally, a global spatial pyramid pooling-fast (Global SPPF) module, equipped with a global receptive field, was designed to enhance boundary-level features and promote the modeling of high-order semantic correlations during feature extraction processing on the backbone. Finally, a lightweight attention-guided weighted downsampling (LAWD) module was proposed to retain critical information as effectively as possible from low-quality input samples. The detection performance of the proposed model was systematically evaluated through ablation studies, comparative analyses, and robustness testing via image augmentation on the collected dataset. Experimental results demonstrate that the proposed model achieves state-of-the-art recognition performance, with a precision of 90.6%, a recall of 87.6%, and a mAP@50 of 93.4%, respectively, and compared with the baseline model, its number of parameters is reduced by 20.5%, and the floating-point computation is reduced by 20.4%, which provides technical support for the detection and maintenance of foreign object on transmission lines.