Recognition of Bird Species of Gaoligong Mountain using Transfer Learning Based on VGG-16 | IEEE Conference Publication | IEEE Xplore

Recognition of Bird Species of Gaoligong Mountain using Transfer Learning Based on VGG-16


Abstract:

The study aims to build a high-quality dataset of local bird images specific to Yunnan province and use it to train and evaluate VGGNet model for bird species recognition...Show More

Abstract:

The study aims to build a high-quality dataset of local bird images specific to Yunnan province and use it to train and evaluate VGGNet model for bird species recognition. The research will focus on evaluating the performance of advanced VGGNet architecture, in classifying Yunnan local bird species. Firstly, established a dataset named GMBID, which consists of 5 species of bird. Secondly, we will use VGGnet, which had been pre-trained on ImageNet. In this procedure, we will use two ways to train these models: re-training and fine-tuning pre-trained model on training set. In this procedure, we focused on using transfer learning based on VGG16 model. Lastly, the testing of the trained model is performed to examine the performance.
Date of Conference: 19-21 January 2024
Date Added to IEEE Xplore: 22 April 2024
ISBN Information:
Conference Location: Guangzhou, China

I. Introduction

In the era of achieving long-term sustainable development of human society, ecological environment protection should be regarded as an important subject, and active efforts should be made to strengthen research, exploration and practice in this field. Accurate identification of these bird species is crucial for understanding their distribution, behavior, and population dynamics. Traditional methods of bird identification heavily rely on manual observation and field expertise, which are not only time-consuming but also prone to subjective factors. This limitation hinders efficient development and accurate identification of large-scale bird data. However, recent advancements in deep learning technology had opened up new possibilities for efficient and reliable image recognition, offering a promising avenue for the study of local bird species in Yunnan province.

References

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