In today's rapidly evolving smart camera technology

animal recognition has emerged as one of its significant applications

showcasing immense potential across various fields. This technological innovation not only enhances the functionality and practicality of smart cameras but also establishes a solid foundation for their widespread application in ecological protection

security monitoring

and other areas. However

existing high-precision animal recognition models are computationally complex and demand substantial hardware resources

making real-time operation on embedded devices challenging and leading to several application difficulties. Therefore

incorporating lightweight techniques into animal recognition algorithms can significantly broaden the application scope of smart camera animal recognition technology while also reducing resource consumption and improving algorithm accuracy. This paper summarizes relevant articles and proposes the introduction of lightweight techniques to optimize algorithms

aiming to provide comprehensive and in-depth theoretical guidance and practical reference for the application of smart camera animal recognition technology.