Channel-boosted multi-scale R-CNN for accurate and real-time ship and land detection in complex SAR scenes

Accurate ship detection in Synthetic Aperture Radar (SAR) imagery is crucial for maritime surveillance but remains challenging due to small target sizes, speckle noise, and complex sea-surface backgrounds. While most existing methods focus exclusively on identifying ships, our approach also achieves reliable detection of land areas, providing an additional contribution to the literature. This study introduces CBM-RCNN (Channel-Boosted Multi-Scale R-CNN), a novel deep learning architecture that integrates Convolutional Block Attention Modules (CBAM) and a Bidirectional Feature Pyramid Network (BiFPN) on a ResNet50 backbone. CBAM enhances both spatial and channel-level feature representation, enabling reliable detection of small vessels, while BiFPN fuses multi-scale features bidirectionally, improving accuracy across vessels of different sizes and positions. CBM-RCNN was evaluated against standard Faster R-CNN and YOLOv8 models across diverse maritime scenes, including simple, densely populated, and visually complex scenarios. The model demonstrated superior detection accuracy, balanced class-specific performance, and strong generalization. It effectively resolves overlapping vessels, distinguishes ships from coastal structures, and maintains robustness under challenging SAR-specific noise conditions. Importantly, it achieves inference speeds suitable for near-real-time applications, highlighting practical applicability. By combining attention-driven refinement with multi-scale feature aggregation, CBM-RCNN addresses limitations of prior methods, particularly in small object recognition, complex scene generalization, and simultaneous land detection. This architecture provides a robust framework for automated maritime monitoring and offers a foundation for future improvements in large-scale SAR-based ship detection and environmental surveillance.