A bird species detection method based on YOLO-v5

Zhihao Liao; Ming Tian

doi:10.1117/12.2615310

15 October 2021 A bird species detection method based on YOLO-v5

Zhihao Liao, Ming Tian

Author Affiliations +

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330C (2021) https://doi.org/10.1117/12.2615310
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

The rapid development of image recognition technology has tremendously promoted the research of species detection. Detection of rare species has important implications for protecting biodiversity. This paper proposes a deep learning method to solve bird species detection based on the current powerful algorithm YOLO-v5. The algorithm combines the feature extraction network of CSPDarkNet to extract the feature information of the input bird image and then uses the aggregation network with a pyramid structure for detection to obtain the required semantic information. The experimental dataset comes from the Kaggle platform and is annotated with free software provided by Github. In this experiment, YOLO-v5 and the other five models are compared for the performance of bird species detection. The comparison results shown that YOLO-v5x has the best precision (95.36%) and mAP (91.28%). It can be seen that compared with other models, YOLO-v5x has high recognition accuracy, good robustness and can be better suited in the actual environment.

Citation Download Citation

Zhihao Liao and Ming Tian "A bird species detection method based on YOLO-v5", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330C (15 October 2021); https://doi.org/10.1117/12.2615310

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