Paper ID: 2410.23073

RSNet: A Light Framework for The Detection of Multi-scale Remote Sensing Targets

Hongyu Chen, Chengcheng Chen, Fei Wang, Yuhu Shi, Weiming Zeng

Recent developments in synthetic aperture radar (SAR) ship detection have seen deep learning techniques achieve remarkable progress in accuracy and speed. However, the detection of small targets against complex backgrounds remains a significant challenge. To tackle these difficulties, this letter presents RSNet, a lightweight framework aimed at enhancing ship detection capabilities in SAR imagery. RSNet features the Waveletpool-ContextGuided (WCG) backbone for enhanced accuracy with fewer parameters, and the Waveletpool-StarFusion (WSF) head for efficient parameter reduction. Additionally, a Lightweight-Shared (LS) module minimizes the detection head's parameter load. Experiments on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID) demonstrate that RSNet achieves a strong balance between lightweight design and detection performance, surpassing many state-of-the-art detectors, reaching 72.5\% and 67.6\% in \textbf{\(\mathbf{mAP_{.50:95}}\) }respectively with 1.49M parameters. Our code will be released soon.

Submitted: Oct 30, 2024