Denoising and Feature Enhancement Network for Target Detection Based on SAR Images
Synthetic aperture radar (SAR) is characterized by its all-weather monitoring capabilities and high-resolution imaging. It plays a crucial role in operations such as marine salvage and strategic deployments. However, existing vessel detection technologies face challenges such as occlusion and deform...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/10/1739 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Synthetic aperture radar (SAR) is characterized by its all-weather monitoring capabilities and high-resolution imaging. It plays a crucial role in operations such as marine salvage and strategic deployments. However, existing vessel detection technologies face challenges such as occlusion and deformation of targets in multi-scale target detection and significant interference noise in complex scenarios like coastal areas and ports. To address these issues, this paper proposes an algorithm based on YOLOv8 for detecting ship targets in complex backgrounds using SAR images, named DFENet (Denoising and Feature Enhancement Network). First, we design a background suppression and target enhancement module (BSTEM), which aims to suppress noise interference in complex backgrounds. Second, we further propose a feature enhancement attention module (FEAM) to enhance the network’s ability to extract edge and contour features, as well as to improve its dynamic awareness of critical areas. Experiments conducted on public datasets demonstrate the effectiveness and superiority of DFENet. In particular, compared with the benchmark network, the detection accuracy of mAP75 on the SSDD and HRSID is improved by 2.3% and 2.9%, respectively. In summary, DFENet demonstrates excellent performance in scenarios with significant background interference or high demands for positioning accuracy, indicating strong potential for various applications. |
|---|---|
| ISSN: | 2072-4292 |