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| Auteur principal: | |
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| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2026
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| Sujets: | |
| Accès en ligne: | https://doi.org/10.5281/zenodo.18438339 |
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Table des matières:
- <div>Fine-grained bird species identification from images plays a critical role in biodiversity monitoring, ecological research, and wildlife conservation. However, manual identification is time-consuming, expertise-dependent, and difficult to scale across large geographic regions. This work presents a unified deep learning framework for automated single and multi-species bird identification using image data.</div> <div> </div> <div>The proposed pipeline integrates object detection and fine-grained classification by combining YOLOv8 for bird localization with multiple convolutional neural network classifiers, including ResNet-50, DenseNet-121, and MobileNetV2, for species recognition. The CUB-200-2011 dataset is standardized through YOLO-format bounding-box annotations and an ImageNet-style directory structure to support both detection and classification tasks. Detected bird regions are independently classified, and a majority-voting strategy with confidence-based tie-breaking is employed to generate the final species prediction.</div> <div> </div> <div>Experimental evaluation demonstrates that DenseNet-121 achieves the highest validation accuracy of 78.44%, followed by MobileNetV2 (76.95%) and ResNet-50 (76.07%). The hybrid ensemble approach improves robustness in complex scenarios involving multiple birds, occlusions, and visually similar species.</div> <div> </div> <div>The proposed framework provides a scalable and reproducible solution for automated fine-grained bird identification, supporting large-scale ecological monitoring, biodiversity assessment, and conservation initiatives.</div> <p> </p>