Robust Maritime Object Detection via a Hybrid DINOv2 and YOLOv8n Architecture

Abstract

Maritime object detection remains challenging because of complex sea-surface backgrounds, adverse illumination conditions, and severe class imbalance, especially when safety-critical targets such as search-and-rescue vessels are sparsely represented. To address these challenges, we propose a cascaded hybrid DINOv2-YOLOv8n detection framework for maritime scenes. Rather than relying only on supervised learning from raw RGB images, the proposed method introduces semantic priors from a frozen DINOv2 encoder and projects them into a compact representation for a YOLOv8n-based detector. To improve robustness under diverse maritime conditions, the framework uses a sea-state-aware online augmentation strategy and is trained with the standard YOLO detection objective. Experiments on the Maritime Target Data Sharing Project (MTDSP) dataset show that the proposed framework achieves strong detection performance. Specifically, it obtains an overall mAP@0.5 of 89.4% and a precision of 100% on the test set. For sparse search-and-rescue vessels and rigid offshore structures, it achieves mAP@0.5 scores of 99.5% and 96.3%, respectively. These results indicate that combining foundation-model semantic priors with a lightweight detector can improve the reliability of maritime object detection under complex sea-surface conditions.