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 this https URL.
Abstract (translated)
在遥感图像中，复杂的背景、弱信号目标和小规模物体使得准确检测变得特别具有挑战性，尤其是在成像质量较低的情况下。一种常见的策略是在检测之前整合单幅图像的超分辨率（SR）处理；然而，这种串联管道通常会面临优化目标不一致、特征冗余以及超分辨率与检测之间缺乏有效交互的问题。为了解决这些问题，我们提出了一种以显著性为导向的多任务协作网络 (SDCoNet)，该网络通过隐式的特征共享将超分辨率和检测结合起来，同时保持各自任务的独特性。SDCoNet 使用基于 Swin 变换器的共享编码器，在此编码器中层次化的窗口偏移自注意力支持跨任务特性合作，并适应性地平衡纹理细化与语义表示之间的权衡。 此外，一个多尺度显著性预测模块生成重要度评分以选择关键令牌，从而能够对弱目标区域集中注意，抑制背景杂乱并压制由多任务结合引入的不利特征。而且，我们还提出了一种梯度路由策略来缓解优化冲突。该策略首先稳定检测语义，并随后将超分辨率梯度沿有利于检测的方向进行路由，使框架引导超分辨率分支生成对检测显式有益的高频细节。 在包括 NWPU VHR-10-Split、DOTAv1.5-Split 和 HRSSD-Split 在内的公共数据集上的实验表明，所提出的方法在低质量遥感图像的小目标检测方面显著优于现有的主流算法，同时保持了具有竞争力的计算效率。我们的代码可在 [这里](https://this https URL) 获取。
URL
https://arxiv.org/abs/2601.12507
