A Unified Object Detection Method in Drone View With Degradation‐Aware and Domain Adaptive Modeling

ABSTRACT Existing object detection methods remain severely challenged by adverse weather and domain shifts. On the one hand, the significant distribution shift between clean and degraded samples under diverse weather conditions prevents models from robustly learning intrinsic object representations. On the other hand, drones are distant from objects, and even slight degradation may lead to significant loss of details. There is a lack of a unified and effective all‐weather detection framework. To this end, a unified object detection method with degradation‐aware and domain adaptive modeling is proposed. First, we design a degradation‐aware module (DAM) that leverages amplitude characteristics in the frequency domain to explicitly model degradation patterns, enabling the detector to perceive various types of image quality deterioration. Second, we propose a domain‐aware attention‐based restoration expert system (DA‐RES). It disentangles shared and domain‐specific representations through a combination of domain‐shared and domain‐specific encoders, thereby suppressing category‐irrelevant information while enhancing domain‐specific useful cues. Finally, through embedding the degradation patterns identified by DAM into the target domain encoder, DA‐RES performs multiscale feature restoration guided by degradation priors, thereby boosting downstream detection tasks against adverse conditions. Extensive experiments demonstrate that the proposed framework achieves robust detection performance under all‐weather conditions, particularly in challenging degraded scenarios.