YOLO-OSD: Optimized Ship Detection and Localization in Multiresolution SAR Satellite Images Using a Hybrid Data-Model Centric Approach

Abstract

With the advancements in space technology and the development of lightweight synthetic aperture radar (SAR) satellites by commercial companies, such as ICEYE, Capella Space and Umbra, SAR images have become available on a wide scale. Ship detection is a classic problem in the interpretation and analysis of satellite images and has its significance both in maritime as well as defense applications. In the case of SAR images, ship detection becomes even more challenging due to the presence of large-scale distortions as well as interclass similarity signature problem. Moreover, the state-of-the-art (SOTA) object detection models have weak generalization capability over SAR datasets. To overcome these challenges, we propose a You Only Look Once (YOLO)-based optimized ship detection model called YOLO-OSD. Our optimized ship detector is based on a hybrid data-model centric approach, which utilizes the statistical characteristics of the datasets under observation and has an efficient model architecture. We also carry out a detailed comparative analysis of our proposed model with other SOTA deep learning models on three well-known publicly available datasets. Our results show that the proposed YOLO-OSD outperforms YOLO5, YOLO7, and RetinaNet on all datasets under observation in terms of F1 score and mean average precision. YOLO-OSD also has approximately 16% fewer network parameters as compared with the original YOLO5. Moreover, our proposed model is at least 37.7% faster than YOLO7 and 41.02% faster than the YOLO8 model in terms of training time and thus suitable for real-time satellite-based SAR ship detection.

Keywords

• Bounding boxes (B-Boxes)
• deep learning (DL)
• multiresolution satellite images
• ship detection
• synthetic aperture radar (SAR)
• you only look once (YOLO)