This is the repository for the paper "Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network". This paper has been officially accepted by IEEE Transactions on Automation Science and Engineering.
This paper benchmarks the cross-domain MAV detection problem. We first propose a Multi-MAV-Multi-Domain (M3D) dataset and construct a novel domain adaptive MAV detection benchmark consisting of three representative domain adaptation tasks, i.e., simulation-to-real adaptation, cross-scene adaptation, and cross-camera adaptation. Moreover, we propose a novel noise suppression network with a prior-guided curriculum learning module, a masked copypaste augmentation module, and a large-to-small model training procedure.
The framework of each training stage in the noise suppression network.
The OneDrive link to the dataset present in the paper: Dataset
The Baidu Netdisk link is: M3D-Sim提取码：4pea; M3D-Real提取码：6ywk; Cropped images提取码：idyh
This dataset includes simulation images and realistic images. All the labels are in the YOLO format. Please refer https://github.com/ultralytics/yolov5 for details.
Samples from the proposed Multi-MAV-Multi-Domain (M3D) dataset.
The top to bottom shows examples from the M3D-Sim subset and M3D-Real
subset, respectively.
The code for mAP evaluation adopts a standard evaluation tool:https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py instead of the code provided by yolov5 for fairness.
If you have any problem when using this dataset, please feel free to contact: zhangyin@westlake.edu.cn.