ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments

Synthetic Aperture Radar (SAR) image ship detection faces challenges such as distinguishing ships from other terrains and structures, especially in specific marine complex environments. The motivation behind this work is to enhance detection accuracy while minimizing false positives, which is crucial for applications like defense vessel monitoring and civilian search and rescue operations. To achieve this goal, we propose several architectural improvements to You Only Look Once version 8 Nano (YOLOv8n) and present Small Target-YOLOv8(ST-YOLOv8)—a novel lightweight SAR ship detection model based on the enhance YOLOv8n framework. The C2f module in the backbone’s transition sections is replaced by the Conv_Online Reparameterized Convolution (C_OREPA) module, reducing convolutional complexity and improving efficiency. The Atrous Spatial Pyramid Pooling (ASPP) module is added to the end of the backbone to extract finer features from smaller and more complex ship targets. In the neck network, the Shuffle Attention (SA) module is employed before each upsampling step to improve upsampling quality. Additionally, we replace the Complete Intersection over Union (C-IoU) loss function with the Wise Intersection over Union (W-IoU) loss function, which enhances bounding box precision. We conducted ablation experiments on two widely used multimodal SAR datasets. The proposed model significantly outperforms the YOLOv8n baseline, achieving 94.1% accuracy, 82% recall, and 87.6% F1 score on the SAR Ship Detection Dataset (SSDD), and 92.7% accuracy, 84.5% recall, and 88.1% F1 score on the SAR Ship Dataset_v0 dataset (SSDv0). Furthermore, the ST-YOLOv8 model outperforms several state-of-the-art multi-scale ship detection algorithms on both datasets. In summary, the ST-YOLOv8 model, by integrating advanced neural network architectures and optimization techniques, significantly improves detection accuracy and reduces false detection rates. This makes it highly suitable for complex backgrounds and multi-scale ship detection. Future work will focus on lightweight model optimization for deployment on mobile platforms to broaden its applicability across different scenarios.