Advancing Fauna Conservation through Machine Learning-Based Spectrogram Recognition: A Study on Object Detection using YOLOv5

Abstract

The protection and monitoring of fauna species are essential for maintaining biodiversity and ensuring the sustainability of ecosystems. Traditional methods of fauna conservation and habitat monitoring rely heavily on manual observation and data collection, which can be time-consuming, and labor-intensive. In recent years, the application of machine learning techniques, such as object detection, has shown great potential in automating the identification of fauna species. In this study, we propose an approach to advancing fauna conservation through the utilization of machine learning-based spectrogram recognition. Specifically, we employ an object detection algorithm, YOLOv5, to detect and classify fauna species from spectrogram images obtained from acoustic recordings. The spectrograms provide a visual representation of audio signals, capturing distinct patterns and characteristics unique to different fauna species. Through extensive experimentation and evaluation, our approach achieved promising results, demonstrating a precision of 0.95, recall of 0.98, F1 score of 0.91, and mean Average Precision (mAP) of 0.934. These performance metrics indicate a high level of accuracy and reliability in fauna species detection. By automating the identification process, our approach provides a scalable solution for monitoring fauna populations over large geographical areas and enables the collection of comprehensive data, facilitating better decision-making and targeted conservation strategies.