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<p>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.</p>

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FDMNet: frequency-domain modulation network for robust object detection in hazy aerial imagery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月8日

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  • Xiaoxiong Zhou,
  • Guangming Zhang,
  • Zhihan Shi,
  • Shanshan Huang,
  • Xiang Cheng

摘要

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.