Remote sensing object detection (RSOD) often suffers from degradations such as low spatial resolution, sensor noise, motion blur, and adverse illumination. These factors diminish feature distinctiveness, leading to ambiguous object representations and inadequate foreground-background separation. Existing RSOD methods exhibit limitations in robust detection of low-quality objects. To address these pressing challenges, we introduce LEGNet, a lightweight backbone network featuring a novel Edge-Gaussian Aggregation (EGA) module specifically engineered to enhance feature representation derived from low-quality remote sensing images. EGA module integrates: (a) orientation-aware Scharr filters to sharpen crucial edge details often lost in low-contrast or blurred objects, and (b) Gaussian-prior-based feature refinement to suppress noise and regularize ambiguous feature responses, enhancing foreground saliency under challenging conditions. EGA module alleviates prevalent problems in reduced contrast, structural discontinuities, and ambiguous feature responses prevalent in degraded images, effectively improving model robustness while maintaining computational efficiency. Comprehensive evaluations across five benchmarks (DOTA-v1.0, v1.5, DIOR-R, FAIR1M-v1.0, and VisDrone2019) demonstrate that LEGNet achieves state-of-the-art performance, particularly in detecting low-quality objects.The code is available at https://github.com/AeroVILab-AHU/LEGNet.

翻译：遥感目标检测（RSOD）常受空间分辨率低、传感器噪声、运动模糊及不利光照等退化因素影响。这些因素降低了特征的区分度，导致目标表征模糊以及前景-背景分离不足。现有RSOD方法在低质量目标的鲁棒检测方面存在局限。为应对这些紧迫挑战，我们提出了LEGNet——一种轻量级骨干网络，其核心是新颖的边缘-高斯聚合模块，专门设计用于增强从低质量遥感图像中提取的特征表示。EGA模块整合了：（a）方向感知的Scharr滤波器，用于锐化在低对比度或模糊目标中常丢失的关键边缘细节；（b）基于高斯先验的特征细化，以抑制噪声并规范化模糊的特征响应，从而在挑战性条件下增强前景显著性。EGA模块缓解了退化图像中普遍存在的对比度降低、结构不连续和特征响应模糊等问题，在保持计算效率的同时有效提升了模型鲁棒性。在五个基准数据集上的综合评估表明，LEGNet实现了最先进的性能，尤其在检测低质量目标方面表现突出。代码已开源。