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

27Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.
Get full text

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 1 is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset 2, the baseline code 3, and the website for evaluation on the public testset 4 are publicly available.

Cite

CITATION STYLE

APA

Kondo, Y., Ukita, N., Yamaguchi, T., Hou, H. Y., Shen, M. Y., Hsu, C. C., … Yasui, S. (2023). MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results. In Proceedings of MVA 2023 - 18th International Conference on Machine Vision and Applications. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.23919/MVA57639.2023.10215935

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free