Small object detection (SOD) is a crucial research area in the field of computer vision. It poses significant challenges due to variations in scale, dense objects, limited target resolution, and a complex background. To achieve real-time detection, existing methods typically focus on local feature extraction and employ downsampling to reduce computation. However, this approach lowers the feature map resolution and loses the fine-grained details during downsampling. Meanwhile, frequency, global, and surrounding information play a significant role in small object detection. To address multi-scale dense targets in RGB and thermal infrared imagery with limited spatial, contextual, and frequency information, we propose a novel architecture that leverages Wavelet Transform Fusion (WTF) and Context-Guided Downsampling (CGD) in the real-time detection transformer (RT-DETR) for small object detection. WTF performs multi-frequency feature decomposition and fusion to preserve both high-frequency details and low-frequency semantic information, thereby improving the representation of small targets while reducing computational complexity. CGD incorporates local, surrounding, and global contextual information during downsampling to mitigate spatial information loss and strengthen feature representation for precise object localization. CGD is a downsampling technique that efficiently captures and preserves the contextual spatial information of local and global features using a local feature extractor and a joint feature extractor. It takes into account the surrounding information of the object, thereby reducing the loss of spatial details during downsampling. This spatial information helps in the precise detection of small objects in RGB and thermal infrared aerial images. Our proposed model is evaluated independently on the RGB aerial dataset NWPU-VHR-10 and the thermal infrared dataset HIT-UAV. Evaluations on the NWPU-VHR-10 and HIT-UAV datasets demonstrate that CGWT-DETR improves the mAP@0.50 to 89.9% and 86.5%, respectively, while boosting the strict localization metric mAP@0.50:0.95 to 60.3% and 58.8%. Furthermore, these accuracy gains are achieved alongside a 14.29% reduction in model parameters and a 29.8% decrease in GFLOPs. Experimental results demonstrate that CGWT-DETR outperforms the RT-DETR baseline in both detection accuracy and computational efficiency.

Small aerial object detection under adverse weather conditions, such as rain, fog, snow, and frost, remains a major challenge in computer vision. Although existing methods perform well for conventional object detection under adverse weather, a gap persists due to the lack of suitable public datasets targeting small aerial objects. Developing a deep learning model capable of restoring weather-degraded images and accurately detecting small objects is difficult because of their low resolution and the background noise introduced by weather effects. To address this limitation, we construct DOTA-Wx, a synthesized weather-augmented dataset derived from DOTA-v1.0, incorporating multiple weather effects specifically for small aerial object detection. To the best of our knowledge, DOTA-Wx is the first dataset developed for this task. In addition, we propose WAGAN-FPN (Weather-Adaptive GAN-based Feature Pyramid Network), an end-to-end framework with a two-stage (restoration and detection) pipeline, which introduces a Weather-Adaptive Generator (WAG) integrating convolutional layers with adaptive attention blocks to restore degraded images while preserving features essential for small-object detection. We evaluate DOTA-Wx using 17 deep learning models published between 2020 and 2025, and compare their performance with WAGAN-FPN. Results show that WAGAN-FPN achieves 68.52% mAP on DOTA-Wx and 82.23 % mAP on DOTA-v1.0, outperforming existing methods by effectively handling weather distortions and maintaining higher detection accuracy.