Biodiversity Information Science and Standards :
Conference Abstract
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Corresponding author: Congtian Lin (linct@ioz.ac.cn), Liqiang Ji (ji@ioz.ac.cn)
Received: 09 Sep 2023 | Published: 11 Sep 2023
© 2023 Congtian Lin, Jiangning Wang, Liqiang Ji
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Lin C, Wang J, Ji L (2023) An AI-based Wild Animal Detection System and Its Application. Biodiversity Information Science and Standards 7: e112456. https://doi.org/10.3897/biss.7.112456
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Rapid accumulation of biodiversity data and development of deep learning methods bring the opportunities for detecting and identifying wild animals automatically, based on artificial intelligence. In this paper, we introduce an AI-based wild animal detection system. It is composed of acoustic and image sensors, network infrastructures, species recognition models, and data storage and visualization platform, which go through the technical chain learned from Internet of Things (IOT) and applied to biodiversity detection. The workflow of the system is as follows:
For storing and exchanging records of machine observations and information of sensors, and models and key nodes of network, we have proposed a collection of data fields extended from Darwin Core and built up a data model to represent where, when and which sensors observe which species. The system has been applied in several projects since last year. For example, we have deployed 50 sensors across the city of Beijing for detecting birds, and now they have harvested more than 300 million records and detected 320 species, filling the data gaps of Beijing birds from taxonomic coverage to time dimension effectively. Next steps will focus on improving AI models for identifying species with higher accuracy, popularizing this system in biodiversity detection, and building up a mechanism for sharing and publishing machine observations.
artificial intelligence, machine observation, workflow, sensor
Congtian Lin
TDWG 2023
We thank Yan Han, members of Institute of Zoology, CAS, for their contributions on collecting data. This job is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No. XDA19050202).
Institute of Zoology, CAS