Beyond Dehazing: Learning Intrinsic Hazy Robustness for Aerial Object Detection

Accurate object detection in aerial imagery is crucial across numerous applications. However, haze can significantly degrade the performance of normal detectors, presenting a substantial obstacle in real-world scenarios. Previous solutions often resort to image dehazing as a pre-processing step to enhance image quality for subsequent detection. Despite being logically intuitive, their performance is limited due to the inherent objective mismatch between low-level image restoration tasks and high-level object detection tasks. In this article, we present haze-robust aerial object detection (HRAOD) to directly enhance detection robustness under hazy conditions. HRAOD constructs a clean-to-hazy distillation framework, enabling the detector to "see through haze," without relying on the explicit image dehazing process. To address the challenge of extracting informative hazy features from blurry and low-contrast hazy images, we introduce a gradient-guided feature imitation method to emphasize the desired objects. Moreover, recognizing that different regions suffer from varying degradation degrees and pose distinct detection difficulties, we further propose a degradation-weighted response distillation method to mimic the normal predictions according to the degradation pattern adaptively. Due to the scarcity of hazy aerial data, we curate two remote sensing hazy aerial datasets, namely DOTA-Haze and SODA-A-Haze, and one drone hazy aerial dataset, DroneVehicle-Haze, for simulation. Extensive experimental results demonstrate the superiority of our method. Specifically, our HRAOD outperforms the state-of-the-art "dehaze + detect" method by 13.1 points in mAP on the DOTA-Haze dataset without incurring additional inference costs. HRAOD also performs favorably against other methods on real-world hazy scenes.