Denoising-Enhanced YOLO for Robust SAR Ship Detection
This work addresses ship detection in SAR imagery for applications like maritime surveillance, but it is incremental as it builds on YOLOv8 with targeted improvements.
The paper tackles robust ship detection in synthetic aperture radar (SAR) imagery by proposing CPN-YOLO, which enhances YOLOv8 with denoising, attention mechanisms, and a new loss function, achieving 98.9% mAP on SSDD and outperforming other detectors.
With the rapid advancement of deep learning, synthetic aperture radar (SAR) imagery has become a key modality for ship detection. However, robust performance remains challenging in complex scenes, where clutter and speckle noise can induce false alarms and small targets are easily missed. To address these issues, we propose CPN-YOLO, a high-precision ship detection framework built upon YOLOv8 with three targeted improvements. First, we introduce a learnable large-kernel denoising module for input pre-processing, producing cleaner representations and more discriminative features across diverse ship types. Second, we design a feature extraction enhancement strategy based on the PPA attention mechanism to strengthen multi-scale modeling and improve sensitivity to small ships. Third, we incorporate a Gaussian similarity loss derived from the normalized Wasserstein distance (NWD) to better measure similarity under complex bounding-box distributions and improve generalization. Extensive experiments on HRSID and SSDD demonstrate the effectiveness of our method. On SSDD, CPN-YOLO surpasses the YOLOv8 baseline, achieving 97.0% precision, 95.1% recall, and 98.9% mAP, and consistently outperforms other representative deep-learning detectors in overall performance.