Beyond animal detection: a benchmark for detecting animal age group

Hang Zhou; You Wu; Jiehong Li; Liangtao Pan; Hengzhou Ye; Shuiwang Li

doi:10.1117/12.3009574

16 October 2023 Beyond animal detection: a benchmark for detecting animal age group

Hang Zhou, You Wu, Jiehong Li, Liangtao Pan, Hengzhou Ye, Shuiwang Li

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031U (2023) https://doi.org/10.1117/12.3009574
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

There are numerous practical applications for animal age estimation, such as age simulation, population structure estimation, wildlife conservation, biometrics, and more. Most of the current methods either concentrate solely on animal detection or animal age estimation. However, animal detection provides limited information, which is not adequate for more sophisticated computer vision tasks. On the other hand, existing methods for estimating animal age are based primarily on facial images, but capturing an accurate image of an animal’s face in a wild environment is extremely difficult and impractical for animals without discriminative facial features. In this paper, we propose a novel task, called Animal Age Group Detection. This task is a combination of animal detection and animal age estimation, with the primary goal of obtaining more information beyond the location of the animal by also providing crude age information, which is simple to accomplish with computer vision methods in a variety of open-world applications. To facilitate the study of this challenging, we present a benchmark for Animal Age Group Detection (AAGD), which contains a diverse range of 22 animal types, comprising a total of 4,778 images. These images are well-annotated with axis-aligned bounding boxes and include information about the age groups of the animals. In addition, we establish a baseline detector for this task, called AG-YOLOF. The release of AAGD aims to facilitate future research on the identification of animal age groups and enhance awareness of this significant area of study.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hang Zhou, You Wu, Jiehong Li, Liangtao Pan, Hengzhou Ye, and Shuiwang Li "Beyond animal detection: a benchmark for detecting animal age group", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031U (16 October 2023); https://doi.org/10.1117/12.3009574

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KEYWORDS

Animals

Object detection

Biological research

Animal model studies

Deep learning

Education and training

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