MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

• URL: http://arxiv.org/abs/2307.09143v1
• Date: Tue, 18 Jul 2023 10:52:24 GMT
• Title: MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
• Authors: 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
• Abstract summary: This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.
• Score: 24.73863448239332
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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