Utilization of deep learning technology in recognizing bird species

Varghese, Aleena; Shyamkrishna, K.; Rajeswari, M.

doi:10.1063/5.0080446

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…Expert identification is both time-consuming and expensive, while radar accuracy is low. Sound recognition struggles with ambient noise interference [8,9]. Advancements in sensor technology enable image-based recognition, offering advantages like reduced environmental impact, remote capture, and leveraging existing ecological data [10,11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning

Yang,

Shen,

2024

Applied Sciences

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Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications.

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