HazyDet: Open-source Benchmark for Drone-view Object Detection with Depth-cues in Hazy Scenes
<|reference_start|>HazyDet: Open-source Benchmark for Drone-view Object Detection with Depth-cues in Hazy Scenes: Drone-based object detection in adverse weather conditions is crucial for enhancing drones' environmental perception, yet it remains largely unexplored due to the lack of relevant benchmarks. To bridge this gap, we introduce HazyDet, a large-scale dataset tailored for drone-based object detection in hazy scenes. It encompasses 383,000 real-world instances, collected from both naturally hazy environments and normal scenes with synthetically imposed haze effects to simulate adverse weather conditions. By observing the significant variations in object scale and clarity under different depth and haze conditions, we designed a Depth Conditioned Detector (DeCoDet) to incorporate this prior knowledge. DeCoDet features a Multi-scale Depth-aware Detection Head that seamlessly integrates depth perception, with the resulting depth cues harnessed by a dynamic Depth Condition Kernel module. Furthermore, we propose a Scale Invariant Refurbishment Loss to facilitate the learning of robust depth cues from pseudo-labels. Extensive evaluations on the HazyDet dataset demonstrate the flexibility and effectiveness of our method, yielding significant performance improvements. Our dataset and toolkit are available at https://github.com/GrokCV/HazyDet.<|reference_end|>
@article{feng2024hazydet:,
    title={HazyDet: Open-source Benchmark for Drone-view Object Detection with
  Depth-cues in Hazy Scenes},
    author={Changfeng Feng and Zhenyuan Chen and Renke Kou and Guangwei Gao and
  Chunping Wang and Xiang Li and Xiangbo Shu and Yimian Dai and Qiang Fu and
  Jian Yang},
    journal={arXiv preprint arXiv:2409.19833},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19833},
    primaryClass={cs.CV}
}