Abstract To achieve object detection in UAV vision, an object detector is crucial, but haze seriously affects detector performance by physically suppressing high-frequency information, which makes it difficult to detect tiny objects. Traditional “dehazing-then-detection” paradigms are restricted to inconsistencies in tasks and restoration effects. The article provides Frequency-Domain Modulation Network (FDMNet), which is an aerial non-explicit-restoration haze-resistant object detector. Following a physical prior, FDMNet constructs an estimate-compensate architecture. A Frequency Domain Modulation (FDM) module is one that expressly estimates the loss of spectral information, and a dynamic LMF-Kernel adaptively restores the loss of mid- and high-frequency discriminative information. To be effective, we present a physical-semantic consistency loss strategy, with frequency-domain consistency loss guaranteeing physical accuracy and prompt distillation loss guaranteeing semantic consistency. We do not have enough datasets, and, therefore, we build Hazy-DOTA, Hazy-DroneVehicle, and a real-life UAV test set. Through extensive experimentation, FDMNet achieved a 7.9% improvement in mAP scores on the HazyDet dataset compared to baseline models, alongside respective gains of 9.9% and 11.5% on the Hazy-DOTA and Hazy-DroneVehicle datasets. Furthermore, it attained state-of-the-art performance relative to several advanced algorithms, balancing both accuracy and robustness. Data availability https://www.kaggle.com/datasets/xiaoxiongzhou/hazy-datasetsThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Wang, H. Y. et al. Cross-Modal Oriented Object Detection of UAV Aerial Images Based on Image Feature. IEEE Trans. Geosci. Remote Sens. 62, 1–21 (2024). Xie, X. X. et al. Fewer is more: efficient object detection in large aerial images. Sci. China-Information Sci. 67 (1), 112106 (2024). Hou, T. et al. MFEL-YOLO for small object detection in UAV aerial images. Expert Syst. Appl. 291, 128459 (2025). Tan, Z. W., Jiang, Z. G., Guo, C. & Zhang, H. P. WSODet: A Weakly Supervised Oriented Detector for Aerial Object Detection. IEEE Trans. Geosci. Remote Sens.