A2I2 Haze Dataset
Track 1: Object Detection in Haze
About the Dataset
Our challenge is based on the A2I2-Haze, the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery.
- Dataset and baseline report: Arxiv
Training & Evaluation
We provide a total of xxx unpaired hazy/clean frame images clipped from xxx videos. This training data is unlabeled, and the participating teams will be allowed to use any extra labeled/unlabeled training data, but they must state so in their submissions ("Method description" section in Codalab). There will be 300 validation data and 200 testing data, both annotated. Imagery and metadata were collected from aerial, ground platforms, and stationary sensors. The target objects include civilian vehicles, mannequins, and potential man-made obstacles encountered during unmanned ground vehicle (UGV) maneuvers such as traffic cones, barriers, barricades, etc. The ranking criteria will be the Mean average precision (mAP) on each testing set, with Interception-of-Union (IoU) threshold as 0.5. If the ratio of the intersection of a detected region with an annotated face region is greater than 0.5, a score of 1 is assigned to the detected region, and 0 otherwise. When mAPs with IoU as 0.5 are equal, the mAPs with higher IoUs (0.6, 0.7, 0.8) will be compared sequentially.
- Paper: ArXiv
- Release Date: December, 2021
- Download Training Data: A2I2-Haze
- Download Dry-run data: Google drive link Baiduyun link(extract code: ???)
- Example submitted dry-run results: Google drive link Baiduyun link (extract code: a6rh)
- Codalab: https://competitions.codalab.org/competitions/????
If you have any questions about this challenge track please feel free to email firstname.lastname@example.org