A review of YOLOv11 based on sar ship detection

Abstract

Synthetic Aperture Radar (SAR) imaging technology holds significant value in military reconnaissance and maritime monitoring due to its all-weather and all-time imaging capabilities. Ship detection, as a core task of maritime monitoring, plays a crucial role in ensuring maritime safety, combating illegal fishing, protecting the marine environment, and military target reconnaissance. However, SAR images inherently suffer from noise, difficulties in detecting small targets, and interference from complex sea conditions, which pose challenges to the design of ship detection algorithms. In recent years, the YOLO series of algorithms has continuously evolved in SAR ship detection, with the latest version, YOLOv11, significantly improving detection accuracy and efficiency through innovations such as lightweight design, multi-scale feature modeling, and improved attention mechanisms. This paper analyzes the key technological features of YOLOv11 and its performance advantages in SAR ship detection while exploring its application prospects in complex scenarios and future optimization directions.