DSONet: A Lightweight Framework for the Detection of Multiscale Dense Ship Occlusion

With the rise of autonomous shipping and intelligent maritime surveillance, accurately detecting ships under multiscale dense occlusions has become a critical challenge. Traditional detectors struggle with feature degradation caused by overlapping targets and scale variation. To address this, we propose DSONet, a lightweight framework tailored for robust ship detection in complex maritime environments. Specifically, we design a task-specific feature extraction structure, DualFeatureDetection (DF-Det), which enhances spatial detail preservation while reducing redundant computation. Additionally, we introduce the AdaptiveUpsample (AU) module to improve multiscale feature fusion and spatial reconstruction, especially under occlusion. Integrated with a four-branch oriented bounding box (OBB) detection head, DSONet achieves precise localization of elongated and overlapping ships. Extensive experiments on the MID, SeaShip and SSDD datasets demonstrate that DSONet outperforms existing detectors in both accuracy and efficiency, offering a practical solution for maritime occlusion detection.