The rapid expansion of mussel raft aquaculture has increased the demand for accurate and scalable monitoring systems to support sustainable coastal management. Deep learning (DL) object detectors, particularly the YOLO family, enable automated extraction of aquaculture infrastructures from Very High-Resolution (VHR) satellite imagery. However, conventional detectors rely on horizontal bounding boxes that often include excessive background when objects are elongated or rotated, reducing localization accuracy. Oriented bounding box (OBB) detectors address this limitation by modeling object orientation and geometry. In this study, an open-source Python-based graphical user interface (GUI) was developed for automated mussel raft detection, and a comprehensive comparison of YOLOv8-OBB and YOLO11-OBB architectures was conducted using VHR imagery from Spain’s Ría de Arousa. A dedicated mussel raft dataset was compiled and annotated, and both model families were evaluated across five architectural scales (n, s, m, l, and x). Experimental results showed that the YOLO11 family generally outperformed YOLOv8 in terms of detection accuracy, localization quality, and performance under challenging maritime conditions. Among the evaluated models, YOLO11s achieved the highest F1-score (0.866) and mAP50–90 (85.21%), while YOLO11n achieved the highest Mean IoU (0.872) and mAP50 (96.87%). The results further demonstrated that compact architectures can provide a favorable balance between accuracy and computational efficiency, as larger models did not consistently yield superior performance. Large-scale experiments on a 60,309 × 61,569-pixel (50 cm resolution) VHR scene demonstrated the operational scalability of the proposed framework through a patch-based processing strategy. The comparative evaluation conducted in this study provides valuable insights into recent YOLO-OBB architectures and highlights their potential for scalable AI-based aquaculture monitoring.