Bird Detection and Species Classification: Using YOLOv5 and Deep Transfer Learning Models

Abstract

—Bird detection and species classification are important tasks in ecological research and bird conservation efforts. The study aims to address the challenges of accurately identifying bird species in images, which plays a crucial role in various fields such as environmental monitoring, and wildlife conservation. This article presents a comprehensive study on bird detection and species classification using the YOLOv5 object detection algorithm and deep transfer learning models. The objective is to develop an efficient and accurate system for identifying bird species in images. The YOLOv5 model is utilized for robust bird detection, enabling the localization of birds within images. Deep transfer learning (TL) models, including VGG19, Inception V3, and EfficientNetB3, are employed for species classification, leveraging their pre-trained weights and learned features. The experimental findings show that the proposed approach is effective, with excellent accuracy in both bird detection and tasks for species classification. The study showcases the potential of combining YOLOv5 with deep transfer learning models for comprehensive bird analysis, opening avenues for automated bird monitoring, ecological research, and conservation efforts. Furthermore, the study investigated the effects of optimization algorithms, including SGD, Adam, and Adamax, on the performance of the models. The findings contribute to the advancement of bird recognition systems and provide insights into the performance and suitability of various deep transfer learning architectures for avian image analysis.