Ship detection and identification in SDGSAT-1 glimmer images based on the glimmer YOLO model

Abstract

ABSTRACTRemote sensing technology has been widely used for marine monitoring. However, due to the limitations of sensor technologies and data sources, effective monitoring of marine ships at night remains challenging. To address these challenges, our study developed SDGST, a high-resolution glimmer marine ship dataset from SDGSAT-1 satellite and proposed a ship detection and identification method based on the YOLOv5s model, the Glimmer YOLO model. Considering the characteristics of glimmer images, our model has made several effective improvements to the original YOLOv5s model. In particular, the improved model incorporates a new layer for detecting small targets and integrates the CA (Coordinate Attention) mechanism. To enhance the original feature fusion strategy, we introduced BiFPN (Bi-directional Feature Pyramid Network). We also adopted the EIOU Loss function and replaced the initially defined anchors with clustering results, thus improving detection performance. The mean Average Precision (mAP%) reaches 96.7%, which is a 5.1% improvement over the YOLOv5s model. Notably, it significantly improves the detection of small ships. This model demonstrates superior performance in ship detection under glimmer conditions compared to the original YOLOv5s model and other popular target detection models, and may serve as a valuable reference for achieving high-precision nighttime marine monitoring.