Deep learning-based foreign object detection methods for transmission lines have become a critical way for continuous and stable operation of next-generation power grids. To enhance detection performance for foreign objects on power transmission lines， a hypergraph-based global-perspective position-aware foreign object detection model was proposed using Hyper-YOLO’s ability to model and propagate high-order semantic correlations across levels and positions. Firstly， a multi-path feature aggregation network with position awareness — PMFA （Position-aware Multi-branch Feature Aggregation） was introduced to position critical features needed to be represented. Additionally， a Global SPPF （Global Spatial Pyramid Pooling-Fast） module with a global receptive field was designed to enhance boundary features extracted by the backbone， so as to promote the modeling and learning abilities of the model to high-order semantic correlations. Finally， an attention-guided LAWD （Lightweight Attention-guided Weighted Downsampling） module was proposed to retain critical information as more as possible from low-quality data samples. The effectiveness and superiority of foreign object detection in transmission line scenarios of the proposed model were verified through ablation studies， comparative analyses， and data augmentation on the constructed dataset of foreign object images of transmission lines. Experimental results demonstrate that the proposed model achieves a precision of 90.6%， a recall of 87.6%， and a mAP@50 of 93.4%， and compared with the baseline model， it has the number of parameters reduced by 20.5%， and the floating-point operations reduced by 20.4%， providing technical support for the detection and management of foreign objects on transmission lines.