Towards Accurate Maritime Surveillance: A Hybrid CNN-Transformer Architecture for Ship Detection in SAR Imagery
Annotatsiya
Finding ships in water using high-resolution radar images is goal of synthetic aperture radar (SAR) ship detection, which works in any lighting or weather scenario. By taking advantage of ships' distinct scattering characteristics against water's surface, it is an essential tool for border security, maritime surveillance, and monitoring illicit fishing or trafficking. To accurately recognize ships in synthetic aperture radar (SAR) images in real-time, this study presents AMTNet, a new attention-enhanced multiscale transformer network. Despite abundance of structural information in SAR images, object detection remains a tough task due to speckle noise and diverse backdrops. suggested AMTNet integrates a ResNet-based multiscale convolutional backbone, a spatial attention module (SAM), a channel attention module (CAM), and transformer-based contextual modeling to solve these problems. In addition, a feature exchange system is implemented to address issue of domain gaps that might be generated by differences in environment and sensors in bi-temporal SAR images. To improve accuracy of change detection and localization, this Siamese design allows cooperative exploitation of spatial, temporal, and contextual signals. Compared to top models like MSDFF-Net, GLDet, and LEAD-YOLO, AMTNet outperforms them all on SSDD dataset, with metrics like detection speed (138.5 FPS), accuracy (94.60 percent), recall (95.15 percent), and F1-score (74.87%). Attention processes and feature exchange have been proven to play a key role in ablation research. Another proof of model's practicality is its resilience in face of occlusion and noise. With its excellent performance in a variety of SAR imaging settings and its high interpretability, AMTNet is a computationally economical and effective option for marine surveillance.