AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports

16th Asian Conference on Computer Vision (ACCV)

Hongyu Sun, Yongcai Wang*, Xudong Cai, Peng Wang, Zhe Huang, Deying Li, Yu Shao, Shuo Wang

School of Information, Renmin University of China, Beijing, 100872

image-20240529183359317 image-20240529183422179 image-20240529183517490

Overview

One fundamental limitation to the research of bird strike prevention is the lack of a large-scale dataset taken directly from real-world airports. Existing relevant datasets are either small in size or not dedicated for this purpose. To advance the research and practical solutions for bird strike prevention, in this paper, we present a large-scale challenging dataset AirBirds that consists of 118,312 time-series images, where a total of 409,967 bounding boxes of flying birds are manually, carefully annotated. The average size of all annotated instances is smaller than 10 pixels in 1920×1080 images. Images in the dataset are captured over 4 seasons of a whole year by a network of cameras deployed at a realworld airport, covering diverse bird species, lighting conditions and 13 meteorological scenarios. To the best of our knowledge, it is the first large-scale image dataset that directly collects flying birds in real-world airports for bird strike prevention. This dataset is publicly available at https://airbirdsdata.github.io/.

overview

Contributions

  • A large-scale dataset, namely AirBirds, that consists of 118,312 time-series images with 1920×1080 resolution containing flying birds in real-world airports is publicly presented, where there exist 409,967 instances with carefully manual bounding box annotations. The dataset covers various kinds of birds in 4 different seasons and diverse scenarios that include day and night, 13 meteorological and lighting conditions, e.g., overcast, sunny, cloudy, rainy, windy, haze, etc.

  • To reflect significant differences with other relevant datasets, we make comprehensive statistics on AirBirds and compare it with relevant datasets. There are three appealing features. (i) The images in AirBirds are dedicatedly taken from a real-world airport, which provide rare first-hand sources for the research of bird strike prevention. (ii) Abundant bird instances in different seasons and changing scenarios are also covered by AirBirds as the data collection spans a full year. (iii) The distribution of AirBirds is distinctive with existing datasets since 88% of instances are smaller than 10 pixels, and the remaining 12% are more than 10 and less than 50 pixels in 1920×1080 images.

  • To understand the difficulty of AirBirds, a wide range of strong baselines are evaluated on this dataset for bird discovering. Specifically, 16 detectors are trained from scratch based on AirBirds with careful configurations and parameter optimization. The consistently unsatisfactory results reveal the non-trivial challenges of bird discovering and bird strike prevention in real-world airports, which deserve further investigation.

AirBirds Construction

This section describes the process of constructing the AirBirds dataset, including raw data collection, subsequent cleaning, annotation, splits and sorting to complete it.

alg1_vis

Statistics

statistics_of_num_images_per_month

statistics_of_num_days_of_different_weather

Comparison to Related Datasets

comparison_with_related_datasets

mn40_so_recognition

mn40_so_recognition

Evaluations

object_detection_airbirds

object_detection_airbirds_coco

comparison_of_alg1_and_yolov5

BibTex 

@inproceedings{sun22airbirds,
  author = {Sun, Hongyu and Wang, Yongcai and Cai, Xudong and Wang, Peng and Huang, Zhe and Li, Deying and Shao, Yu and Wang, Shuo},
  title = {AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports},
  year = {2023},
  isbn = {978-3-031-26347-7},
  publisher = {Springer-Verlag},
  address = {Berlin, Heidelberg},
  url = {https://doi.org/10.1007/978-3-031-26348-4_24},
  doi = {10.1007/978-3-031-26348-4_24},
  booktitle = {Computer Vision – ACCV 2022: 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part V},
  pages = {409–424},
  numpages = {16},
  keywords = {Bird strike prevention, Bird detection in airport, Large-scale dataset},
  location = {Macao, China}
}

Acknowledgment

We thank all members who involved in the system deploying, data collecting, processing and labeling. This work was supported in part by the National Natural Science Foundation of China (Grant No. 61972404, 12071478).