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Related Experiment Video

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Enhancing occluded and standard bird object recognition using fuzzy-based ensembled computer vision approach with

L Richard1, G H Dhruthi1, M Ashwin Kumar1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

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|July 2, 2025
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Summary

This study introduces a fuzzy-based ensemble learning framework using Convolutional Neural Networks (CNNs) to accurately classify bird species, even with occluded images. The novel approach significantly improves bird classification accuracy and reliability for ecological research.

Keywords:
Bird species classificationConvolutional neural networksDeep learningEnsemble learningFuzzy logic

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Area of Science:

  • Ecology and Biodiversity
  • Computer Science and Artificial Intelligence
  • Machine Learning

Background:

  • Accurate bird species classification is crucial for ecological studies and biodiversity conservation.
  • Traditional classification methods are often labor-intensive and error-prone.
  • Convolutional Neural Networks (CNNs) offer a more robust approach to image feature extraction and classification.

Purpose of the Study:

  • To enhance bird species classification accuracy, particularly for occluded subjects, by developing a novel ensemble learning framework.
  • To improve the generalization capability of bird image classification models.
  • To address the limitations of existing methods in handling obstructed bird images.

Main Methods:

  • An ensemble learning framework was developed by integrating top-performing CNN architectures (DenseNet and ResNet families).
  • Fuzzy logic was employed to adaptively allocate model weights based on feature contributions.
  • A dataset of 11,352 bird images from Caltech-UCSD Birds-200-2011 and Birds525 Species-Image Classification datasets was utilized, incorporating advanced augmentation techniques.

Main Results:

  • The proposed fuzzy-based ensemble approach achieved 98.73% accuracy and a 98.75% F1-score for standard images.
  • For occluded bird images, the model attained 95.78% accuracy and a 95.1% F1-score.
  • Performance improvements of 2% for standard and 9% for occluded images were observed compared to existing methods, with 2-7% gains over individual CNN candidates.

Conclusions:

  • The fuzzy-based ensemble learning framework significantly enhances bird species classification accuracy, especially for occluded images.
  • The adaptive weighting mechanism based on fuzzy logic effectively improves model performance and reliability.
  • Statistical validation confirmed the significance of the performance improvements, highlighting the method's potential for ecological applications.