Detecting small objects in high-resolution remote sensing images presents persistent challenges due to their limited pixel coverage, complex backgrounds, and dense spatial distribution. These difficulties are further exacerbated by the increasing resolution and volume of remote sensing data, which impose stringent demands on both detection accuracy and computational efficiency. Addressing these challenges requires the development of lightweight yet robust detection frameworks tailored for small object detection under resource-constrained conditions. To address these challenges, we propose RSNet, a lightweight and highly efficient detection framework designed specifically for small object detection in high-resolution remote sensing images. At its core, RSNet features the Compact-Align Detection Head (CADH), which enhances scale-adaptive localization and improves detection sensitivity for densely distributed small-scale targets. To preserve features during spatial downsampling, we introduce the Adaptive Downsampling module (ADown), which effectively balances computational efficiency with semantic retention. Additionally, RSNet integrates GSConv to enable efficient multi-scale feature fusion while minimizing resource consumption. In addition, we adopt a K-fold cross-validation strategy to enhance the stability and credibility of model evaluation under spatially heterogeneous remote sensing data conditions. To evaluate the performance of RSNet, we conduct extensive experiments on two widely recognized remote sensing benchmarks, DOTA and NWPU VHR-10. The results show that RSNet achieves mean Average Precision (mAP) scores of 76.4% and 92.1%, respectively, significantly surpassing existing state-of-the-art models in remote sensing object detection. These findings confirm RSNet’s ability to balance high detection accuracy with computational efficiency, making it highly suitable for real-time applications on resource-constrained platforms such as satellite-based remote sensing systems or edge computing devices used in remote sensing applications.

Robust scene perception in adverse environmental conditions, particularly under dense fog, presents a persistent and fundamental challenge to the reliability of object detection systems. To address this critical challenge, we propose Fog-UAVNet, a novel lightweight deep-learning architecture designed to enhance unmanned aerial vehicle (UAV) object detection performance in foggy environments. Fog-UAVNet incorporates three key innovations: the Spatial-Edge Feature Fusion Module (SEFFM), which enhances feature extraction by effectively integrating edge and spatial information, the Frequency-Adaptive Dilated Convolution (FADC), which dynamically adjusts to fog density variations and further enhances feature representation under adverse conditions, and the Dynamic Task-Aligned Head (DTAH), which dynamically aligns localization and classification tasks and thus improves overall model performance. To evaluate the effectiveness of our approach, we independently constructed a real-world foggy dataset and synthesized the VisDrone-fog dataset using an atmospheric scattering model. Extensive experiments on multiple challenging datasets demonstrate that Fog-UAVNet consistently outperforms state-of-the-art methods in both detection accuracy and computational efficiency, highlighting its potential for enhancing robust visual perception under adverse weather.