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利用迁移学习方法对本地和外来入侵物种进行爬行动物识别

Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches.

作者信息

Hernández-López Ruymán, Travieso-González Carlos M

机构信息

Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.

出版信息

Sensors (Basel). 2024 Feb 20;24(5):1372. doi: 10.3390/s24051372.

Abstract

The Canary Islands are considered a hotspot of biodiversity and have high levels of endemicity, including endemic reptile species. Nowadays, some invasive alien species of reptiles are proliferating with no control in different parts of the territory, creating a dangerous situation for the ecosystems of this archipelago. Despite the fact that the regional authorities have initiated actions to try to control the proliferation of invasive species, the problem has not been solved as it depends on sporadic sightings, and it is impossible to determine when these species appear. Since no studies for automatically identifying certain species of reptiles endemic to the Canary Islands have been found in the current state-of-the-art, from the Signals and Communications Department of the Las Palmas de Gran Canaria University (ULPGC), we consider the possibility of developing a detection system based on automatic species recognition using techniques. So this research conducts an initial identification study of some species of interest by implementing different neural network models based on transfer learning approaches. This study concludes with a comparison in which the best performance is achieved by integrating the base model, which has a mean of 98.75%.

摘要

加那利群岛被认为是生物多样性热点地区,具有高度的特有性,包括特有爬行动物物种。如今,一些外来入侵爬行动物种正在该地区不同地方不受控制地繁殖,给这个群岛的生态系统造成了危险局面。尽管地区当局已采取行动试图控制入侵物种的繁殖,但问题尚未解决,因为这依赖于零星的目击情况,而且无法确定这些物种何时出现。由于在当前的技术水平下尚未发现用于自动识别加那利群岛某些特有爬行动物种的研究,大加那利岛拉斯帕尔马斯大学(ULPGC)信号与通信系考虑了使用技术开发基于自动物种识别的检测系统的可能性。因此,本研究通过基于迁移学习方法实现不同的神经网络模型,对一些感兴趣的物种进行了初步识别研究。本研究最后进行了比较,其中通过整合基础模型取得了最佳性能,该模型的平均准确率为98.75%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e10/10934242/e94e2c98883f/sensors-24-01372-g0A1.jpg

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