Enhancing, Refining, and Fusing: Towards Robust Multi-Scale and Dense Ship Detection

2025-01-10

AI Summary

To address the challenges of ship detection in SAR imagery—namely complex backgrounds, high ship density, and large scale variations—this paper proposes CASS-Det, a center-aware ship detector. Methodologically, it introduces three novel components: (1) a Rotation-Aware Center Enhancement Module (CEM) that strengthens center-point response via rotated convolution; (2) a Cross-Layer Dependency-Driven Neighborhood Attention Module (NAM) to improve local boundary modeling; and (3) a Cross-Connected Feature Pyramid Network (CC-FPN) for enhanced multi-scale feature fusion. These modules jointly optimize localization accuracy, boundary refinement, and scale robustness. Extensive experiments on SSDD, HRSID, and LS-SSDD-v1.0 demonstrate that CASS-Det consistently outperforms state-of-the-art methods, achieving new SOTA performance in precision and recall for small-scale, high-density, and multi-scale ship detection.

Technology Category

• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Synthetic aperture radar (SAR) imaging, celebrated for its high resolution, all-weather capability, and day-night operability, is indispensable for maritime applications. However, ship detection in SAR imagery faces significant challenges, including complex backgrounds, densely arranged targets, and large scale variations. To address these issues, we propose a novel framework, Center-Aware SAR Ship Detector (CASS-Det), designed for robust multi-scale and densely packed ship detection. CASS-Det integrates three key innovations: (1) a center enhancement module (CEM) that employs rotational convolution to emphasize ship centers, improving localization while suppressing background interference; (2) a neighbor attention module (NAM) that leverages cross-layer dependencies to refine ship boundaries in densely populated scenes; and (3) a cross-connected feature pyramid network (CC-FPN) that enhances multi-scale feature fusion by integrating shallow and deep features. Extensive experiments on the SSDD, HRSID, and LS-SSDD-v1.0 datasets demonstrate the state-of-the-art performance of CASS-Det, excelling at detecting multi-scale and densely arranged ships.

Problem

• Synthetic Aperture Radar (SAR) Imaging
• Ship Detection
• Complex Backgrounds

Innovation

• Center Enhancement Module (CEM)
• Neighbor Attention Module (NAM)
• Cross-Connected Feature Pyramid Network (CC-FPN)