With the rapid advancement of UAV technology, robust object detection under adverse weather conditions has become critical for enhancing UAVs’ environmental perception. However, object detection in such challenging conditions remains a significant hurdle, and standardized evaluation benchmarks are still lacking. To bridge this gap, we introduce the Adverse Weather Object Detection (AWOD) dataset—a large-scale dataset tailored for object detection in complex maritime environments. The AWOD dataset comprises 20,000 images captured under three representative adverse weather conditions: foggy, flare, and low-light. To address the challenges of scale variation and visual degradation introduced by harsh weather, we propose WRRT-DETR, a weather-robust object detection framework optimized for small objects. Within this framework, we design a gated single-head global–local attention backbone block (GLCE) to fuse local convolutional features with global attention, enhancing small object distinguishability. Additionally, a Frequency–Spatial Feature Augmentation Module (FSAE) is introduced to incorporate frequency-domain information for improved robustness, while an Attention-based Cross-Fusion Module (ACFM) facilitates the integration of multi-scale features. Experimental results demonstrate that WRRT-DETR outperforms SOTA methods on the AWOD dataset, exhibiting superior robustness and detection accuracy in complex weather conditions.
WRRT-DETR: Weather-Robust RT-DETR for Drone-View Object Detection in Adverse Weather
2025
Article (Journal)
Electronic Resource
Unknown
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