YOLO-Pole: A Deep Learning Framework for Precise Pole Localization in Aerial Orthophotos

Pole detection in aerial orthophotos is a critical yet challenging task due to the small size of poles (often reduced to just 1-2 pixels), limited vertical profile visibility, and varying lighting conditions in aerial imagery. Existing approaches primarily rely on bounding box detection, which lacks the precision needed for practical applications such as urban infrastructure mapping and autonomous navigation. In contrast, this paper introduces YOLO-Pole, a novel end-to-end deep learning framework based on YOLOv7 architecture, specifically designed for high-precision pole localization in aerial orthophotos. Instead of providing a coarse bounding box, YOLO-Pole directly predicts the precise pole footprint using a single-stage process. To further refine localization, we introduce a pointwise loss function based on Euclidean distance. Experimental results on a custom dataset with 20 cm ground sample distance (GSD) demonstrate significant improvements in localization accuracy of poles over the standard YOLO model, confirming that precise pole localization is achievable and offering potential for imagebased geolocalization.