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HazyDet: Open-Source Benchmark for Drone-View Object Detection With Depth-Cues in Hazy Scenes (paper)

HazyDet is the first benchmark for object detection in hazy drone imagery. It couples physics-driven synthetic data with real foggy drone photos, providing a controlled yet realistic test-bed for designing haze-robust detectors.


Abstract

Object detection from aerial platforms under adverse atmospheric conditions, particularly haze, is paramount for robust drone autonomy. Yet, this domain remains largely underexplored, primarily hindered by the absence of specialized benchmarks. To bridge this gap, we present HazyDet, the first, large-scale benchmark specifically designed for drone-view object detection in hazy conditions. Comprising 383,000 real-world instances derived from both naturally hazy captures and synthetically hazed scenes augmented from clear images, HazyDet provides a challenging and realistic testbed for advancing detection algorithms. To address the severe visual degradation induced by haze, we propose the Depth-Conditioned Detector (DeCoDet), a novel architecture that integrates a Depth-Conditioned Kernel to dynamically modulate feature representations based on depth cues. The practical efficacy and robustness of DeCoDet are further enhanced by its training with a Progressive Domain Fine-Tuning (PDFT) strategy to navigate synthetic-to-real domain shifts, and a Scale-Invariant Refurbishment Loss (SIRLoss) to ensure resilient learning from potentially noisy depth annotations. Comprehensive empirical validation on HazyDet substantiates the superiority of our unified DeCoDet framework, which achieves state-of-the-art performance, surpassing the closest competitor by a notable +1.5% mAP on challenging real-world hazy test scenarios. Our dataset and toolkit are available at github.

HazyDet

HazyDet


πŸ“¦ Dataset at a Glance

Target size buckets: Small < 0.1 % of image area , Medium 0.1–1 % , Large > 1 %

Split #Images #Instances Class Small Medium Large
Train 8 000 264 511 Car 159 491 77 527 5 177
Truck 4 197 6 262 1 167
Bus 1 990 7 879 861
Val 1 000 34 560 Car 21 051 9 881 630
Truck 552 853 103
Bus 243 1 122 125
Test 2 000 65 322 Car 38 910 19 860 1 256
Truck 881 1 409 263
Bus 473 1 991 279
Real-world Train 400 13 753 Car 5 816 6 487 695
Truck 86 204 57
Bus 52 256 100
Real-world Test 200 5 543 Car 2 351 2 506 365
Truck 26 86 30
Bus 17 107 55

You can also download our HazyDet dataset from Baidu Netdisk or OneDrive.

For both training and inference, the following dataset structure is required:

HazyDet

HazyDet/
 β”œβ”€β”€ train/
 β”‚   └── clean images/
 β”‚   └── hazy images/
 β”‚   └── lables/
 β”œβ”€β”€val/
 β”‚   └── clean images/
 β”‚   └── hazy images/
 β”‚   └── lables/
 β”œβ”€β”€ test/
 β”‚   └── clean images/
 β”‚   └── hazy images/
 β”‚   └── lables/
 β”œβ”€β”€ Real-world/
 β”‚   └── train/
 β”‚   └── test/
 β”‚   └── lables/
 └── README.md  <-- you are here 

Note: Both passwords for BaiduYun and OneDrive is grok.

Leadboard and Model Zoo

All the weight files in the model zoo can be accessed on Baidu Cloud and OneDrive.

Detectors

Model Backbone #Params (M) GFLOPs mAP on
Synthetic Test-set
mAP on
Real-world Test-set
Weight
One Stage
YOLOv3 Darknet53 61.63 20.19 35.0 30.7 weight
GFL ResNet50 32.26 198.65 36.8 32.5 weight
YOLOX CSPDarkNet 8.94 13.32 42.3 35.4 weight
FCOS ResNet50 32.11 191.48 45.9 32.7 weight
VFNet ResNet50 32.71 184.32 49.5 35.6 weight
ATTS ResNet50 32.12 195.58 50.4 36.4 weight
DDOD ResNet50 32.20 173.05 50.7 37.1 weight
TOOD ResNet50 32.02 192.51 51.4 36.7 weight
Two Stage
Faster RCNN ResNet50 41.35 201.72 48.7 33.4 weight
Libra RCNN ResNet50 41.62 209.92 49.0 34.5 weight
Grid RCNN ResNet50 64.46 317.44 50.5 35.2 weight
Cascade RCNN ResNet50 69.15 230.40 51.6 37.2 weight
End-to-End
Conditional DETR ResNet50 43.55 91.47 30.5 25.8 weight
DAB DETR ResNet50 43.7 91.02 31.3 27.2 weight
Deform DETR ResNet50 40.01 203.11 51.5 36.9 weight
DeCoDet
DeCoDet (Ours) ResNet50 34.62 225.37 52.0 38.7 weight

Dehazing

Type Method PSNR SSIM mAP on Test-set mAP on RDDTS Weight
Baseline Faster RCNN - - 39.5 21.5 weight
Dehaze GridDehaze 12.66 0.713 38.9 (-0.6) 19.6 (-1.9) weight
Dehaze MixDehazeNet 15.52 0.743 39.9 (+0.4) 21.2 (-0.3) weight
Dehaze DSANet 19.01 0.751 40.8 (+1.3) 22.4 (+0.9) weight
Dehaze FFA 19.25 0.798 41.2 (+1.7) 22.0 (+0.5) weight
Dehaze DehazeFormer 17.53 0.802 42.5 (+3.0) 21.9 (+0.4) weight
Dehaze gUNet 19.49 0.822 42.7 (+3.2) 22.2 (+0.7) weight
Dehaze C2PNet 21.31 0.832 42.9 (+3.4) 22.4 (+0.9) weight
Dehaze DCP 16.98 0.824 44.0 (+4.5) 20.6 (-0.9) weight
Dehaze RIDCP 16.15 0.718 44.8 (+5.3) 24.2 (+2.7) weight

Citation

If you use this toolbox or benchmark in your research, please cite this project.


@article{feng2025HazyDet,
      title={HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenes}, 
      author={Changfeng Feng and Zhenyuan Chen and Xiang Li and Chunping Wang and Jian Yang and Ming-Ming Cheng and Yimian Dai and Qiang Fu},
      year={2025},
      journal={arXiv preprint arXiv:2409.19833}, 
}

@article{zhu2021detection,
  title={Detection and tracking meet drones challenge},
  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={11},
  pages={7380--7399},
  year={2021},
  publisher={IEEE}
}
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