Species Recognition Technology Based on Machine Learning

DOI

10.2991/978-94-6463-512-6_50How to use a DOI?

Keywords

Animal Identification; Machine Learning; Deep Learning; Convolutional Neural Network

Abstract

Animal Recognition Technology has an important role to play in identifying and conserving species. As society continues to progress and people's standard of living improves, it intensifies environmental pollution and ecological damage, which inevitably increases the risk of species extinction, thus increasing the urgent need for species protection. In the face of this challenge, researchers have continued to propose, improve, and refine animal identification techniques to achieve more accurate, faster, and simpler species identification techniques. The purpose of this paper is to explore and summarize existing species identification techniques and provide reference materials for future research. This paper will focus on the following areas: First is the selection of datasets, followed by algorithms for animal detection and recognition techniques, including traditional image processing methods and the latest deep learning techniques. The accuracy and performance of these models will then be evaluated to see how they perform in real-world applications. Finally, the model selection strategy will be explored. This paper aims to provide a detailed reference for researchers in the field of animal identification technology, to help subsequent researchers better understand the strengths and weaknesses of existing techniques, and to provide reference and inspiration for future research. At the same time, the author will present ideas and suggestions to contribute to the technological development of the field to promote the conservation of species and the realization of ecological balance.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qiming Wu
PY  - 2024
DA  - 2024/09/23
TI  - Species Recognition Technology Based on Machine Learning
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 468
EP  - 476
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_50
DO  - 10.2991/978-94-6463-512-6_50
ID  - Wu2024
ER  -