Knowledge distillation-enhanced marine optical remote sensing object detection with transformer and dual-path architecture

With the growing demand for marine surveillance and resource management, accurate marine object detection has become crucial for both military operations and civilian applications. However, this task faces inherent challenges including complex environmental interference, diverse object scales and morphologies, and dynamic imaging conditions. To address these issues, this paper proposes a marine optical remote sensing object detection architecture based on transformer and dual path architecture (MOD-TD), aiming to improve the accuracy and robustness of maritime target detection. The encoder integrates a Holistic Focal Feature Interwined (HFFI) module that employs parallel pathways to progressively refine local textures and global semantic representations, enabling adaptive feature fusion across spatial hierarchies. The decoder introduces task-specific query decoupling for classification and localization, combined with an Enhanced Multi-scale Attention (EMSA) mechanism that dynamically aggregates contextual information from multiple receptive fields. Furthermore, the framework incorporates a Multivariate Matching strategy with Gaussian spatial constraints to improve anchor-object correspondence in complex marine scenarios. To balance detection accuracy with computational efficiency, a knowledge distillation framework is implemented where a compact student model learns distilled representations through multi-granularity alignment with a teacher network, encompassing intermediate feature guidance and output-level probability calibration. Comprehensive evaluations on the SeaDronesSee and DOTA-Marine datasets validate the architecture’s superior detection performance and environmental adaptability compared to existing methods, demonstrating significant advancements in handling multi-scale objects under variable marine conditions. This work establishes a new paradigm integrating architectural innovation and model compression strategies for practical marine observation systems.