MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset,
  Methods, and Results
Yuki Kondo Norimichi Ukita Takayuki Yamaguchi Hao-Yu Hou Mu-Yi Shen Chia-Chi Hsu En-Ming Huang Yu-Chen Huang Yu-Cheng Xia Chien-Yao Wang Chun-Yi Lee Da Huo Marc A. Kastner Tingwei Liu Yasutomo Kawanishi Takatsugu Hirayama Takahiro Komamizu Ichiro Ide Yosuke Shinya Xinyao Liu Guang Liang Syusuke Yasui
Small Object Detection (SOD) is an important machine vision topic because (i)
a variety of real-world applications require object detection for distant
objects and (ii) SOD is a challenging task due to the noisy, blurred, and
less-informative image appearances of small objects. This paper proposes a new
SOD dataset consisting of 39,070 images including 137,121 bird instances, which
is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The
detail of the challenge with the SOD4SB dataset is introduced in this paper. In
total, 223 participants joined this challenge. This paper briefly introduces
the award-winning methods. The dataset, the baseline code, and the website for
evaluation on the public testset are publicly available.