RP-UpsNet:Residual Projection Fusion with Guided Upsampling for Salient Object Detection
IEEE Access, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/access.2026.3739650
- Dergi Adı: IEEE Access
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
- Anahtar Kelimeler: deep learning, feature fusion, residual projection, Salient object detection, upsampling
- Atatürk Üniversitesi Adresli: Evet
Özet
Salient Object Detection (SOD) aims to identify visually salient regions by mimicking human visual attention mechanisms. Despite significant advances in deep encoder-decoder architectures, preserving fine spatial details and accurately detecting small or low-contrast objects remains challenging, particularly due to gradual spatial downsampling. This issue leads to degraded edge quality and structural inconsistencies, particularly in complex scenes and non-central regions. To address these limitations, we propose RP-UpsNet, a new SOD framework that integrates guided upsampling and multi-scale feature fusion to reduce the loss of spatial detail. The framework uses the Compact Multi-Scale CNN (CMS-CNN) backbone and distributes high-resolution, content-sensitive features across multiple network stages.In addition, an additive gate-based fusion mechanism effectively combines semantic and spatial information, while the residual projection module enhances cross-scale feature interactions. Extensive experiments conducted on eight different benchmark datasets demonstrate that the proposed method achieves competitive and consistent improvements in Fβw, Sα, and MAE. The proposed approach demonstrates significant improvements in boundary preservation and structural consistency, particularly in challenging scenarios involving small objects, low-contrast regions, and complex backgrounds.