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

• URL: http://arxiv.org/abs/2410.23073v3
• Date: Sun, 10 Nov 2024 02:31:09 GMT
• Title: RSNet: A Light Framework for The Detection of Multi-scale Remote Sensing Targets
• Authors: Hongyu Chen, Chengcheng Chen, Fei Wang, Yuhu Shi, Weiming Zeng,
• Abstract summary: RSNet is a lightweight framework to enhance ship detection in SAR imagery. Waveletpool-ContextGuided (WCG) is the backbone, guiding global context understanding. Waveletpool-StarFusion (WSF) is introduced as the neck, employing a residual wavelet element-wise multiplication structure.
• Score: 10.748210940033484
• License:
• Abstract: Recent advancements in synthetic aperture radar (SAR) ship detection using deep learning have significantly improved accuracy and speed, yet effectively detecting small objects in complex backgrounds with fewer parameters remains a challenge. This letter introduces RSNet, a lightweight framework constructed to enhance ship detection in SAR imagery. To ensure accuracy with fewer parameters, we proposed Waveletpool-ContextGuided (WCG) as its backbone, guiding global context understanding through multi-scale wavelet features for effective detection in complex scenes. Additionally, Waveletpool-StarFusion (WSF) is introduced as the neck, employing a residual wavelet element-wise multiplication structure to achieve higher dimensional nonlinear features without increasing network width. The Lightweight-Shared (LS) module is designed as detect components to achieve efficient detection through lightweight shared convolutional structure and multi-format compatibility. 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.

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