Bird detection based on improved YOLOv8

Liping Liu

doi:10.1117/12.3051666

18 November 2024 Bird detection based on improved YOLOv8

Liping Liu

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033K (2024) https://doi.org/10.1117/12.3051666
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Automated bird identification systems are becoming increasingly important in biodiversity research and ecological conservation. I n this study, we present a bird recognition approach utilizing an enhanced YOLOv8 model. The detection accuracy and robustness are boosted by integrating the Global Attention Mechanism (GAM) and NAS-FPN into the model. GAM effectively enhances the model's ability to focus on important features, while NAS-FPN optimizes the fusion and extraction of multi-scale features. We tested the enhanced model using the CUA-200-2011 dataset, and the experimental findings indicate that the upgraded YOLOv8 model demonstrates improvements in both recall and accuracy metrics. This research not only enhances the precision of bird recognition but also offers a novel approach for detecting small targets in complex environments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liping Liu "Bird detection based on improved YOLOv8", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033K (18 November 2024); https://doi.org/10.1117/12.3051666

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