SDCoNet: Saliency-Driven Multi-Task Collaborative Network for Remote Sensing Object Detection

• URL: http://arxiv.org/abs/2601.12507v1
• Date: Sun, 18 Jan 2026 17:36:48 GMT
• Title: SDCoNet: Saliency-Driven Multi-Task Collaborative Network for Remote Sensing Object Detection
• Authors: Ruo Qi, Linhui Dai, Yusong Qin, Chaolei Yang, Yanshan Li,
• Abstract summary: In remote sensing images, complex backgrounds, weak object signals, and small object scales make accurate detection particularly challenging.<n>A common strategy is to integrate single-image super-resolution (SR) before detection.<n>We propose a Saliency-Driven multi-task Collaborative Network (SDCoNet) that couples SR and detection through implicit feature sharing.
• Score: 7.016133328153285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In remote sensing images, complex backgrounds, weak object signals, and small object scales make accurate detection particularly challenging, especially under low-quality imaging conditions. A common strategy is to integrate single-image super-resolution (SR) before detection; however, such serial pipelines often suffer from misaligned optimization objectives, feature redundancy, and a lack of effective interaction between SR and detection. To address these issues, we propose a Saliency-Driven multi-task Collaborative Network (SDCoNet) that couples SR and detection through implicit feature sharing while preserving task specificity. SDCoNet employs the swin transformer-based shared encoder, where hierarchical window-shifted self-attention supports cross-task feature collaboration and adaptively balances the trade-off between texture refinement and semantic representation. In addition, a multi-scale saliency prediction module produces importance scores to select key tokens, enabling focused attention on weak object regions, suppression of background clutter, and suppression of adverse features introduced by multi-task coupling. Furthermore, a gradient routing strategy is introduced to mitigate optimization conflicts. It first stabilizes detection semantics and subsequently routes SR gradients along a detection-oriented direction, enabling the framework to guide the SR branch to generate high-frequency details that are explicitly beneficial for detection. Experiments on public datasets, including NWPU VHR-10-Split, DOTAv1.5-Split, and HRSSD-Split, demonstrate that the proposed method, while maintaining competitive computational efficiency, significantly outperforms existing mainstream algorithms in small object detection on low-quality remote sensing images. Our code is available at https://github.com/qiruo-ya/SDCoNet.

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