https://www.mdu.se/

The integration of unmanned aerial vehicles (UAVs) into maritime tasks such as debris detection, vessel monitoring, and tracking of marine life is crucial to advancing autonomous navigation. However, the effectiveness of UAVs depends on robust object detection algorithms, which are currently hindered by the limited scale, diversity, and relevance of existing maritime datasets. To address these limitations, (i) we introduce DIANA (Drone Imagery for Archipelago Navigation and Analysis), the first UAV dataset featuring 17,758 high-resolution images annotated with more than 353,141 objects. This dataset spans diverse maritime environments and contains a wide range of object sizes, from small buoys to large vessels. (ii) Our study assesses the impact of data augmentation techniques and a sliding window function on improving accuracy for small objects, as shown by an increase in AP in DiffusionDet (34.4% to 37.8%) with GAN-based augmentation. (iii) We also present a benchmark of state-of-the-art object detection models, including CNN-based and transformer-based architectures, evaluating performance on small, medium, and large objects. Key findings show that transformer-based models, such as DINO and DiffusionDet, excel in small object detection. On the other hand, one-stage CNN models, such as YOLOv8, are effective for medium-size objects. The source code and dataset are available at https://github.com/TurkuAISLAB/DIANA.