Ultra-high-voltage overhead transmission lines are crucial in power systems. But, they often face accidents triggered by external factors, such as construction activities and wildfires. These incidents not only damage the national economy and affect grid stability, but also pose a threat to the safety of power workers. Deep learning-based object detection methods offer a novel solution for detecting external force damage objects. However, existing methods often rely on local neighborhood information for sampling operations, which limits their perceptual range and expressive capabilities. To address this issue, a real-time global awarenessenhanced method, GAE-YOLO, based on YOLOv10, is proposed to improve the detection accuracy of external force damage objects in ultra-high-voltage overhead transmission lines. To overcome the limitations of local perception in traditional methods, two novel upsampling and downsampling modules are designed, including the global awareness downsampling module (GADM) and the global awareness upsampling module (GAUM). GADM enhances perceptual performance by learning global spatial information from the feature map and generating global perception weights to optimize the downsampling process. GAUM dynamically enhances the membership relationship of sampling points by utilizing channel information from deep feature maps, effectively highlighting object boundaries. To evaluate the effectiveness of GAE-YOLO, a large-scale dataset for detecting external force damages in ultra-high-voltage overhead transmission lines is constructed. The model achieves mAP of 93.05%, a mAP 5.13% improvement over the baseline model. Experimental results show that GAE-YOLO significantly improves the detection accuracy of external-force damage objects, offering substantial application value and providing new technical support for the safe operation of power grids.