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ALGA-DenseNet ground-based cloud classification network based on multi-scale features.

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This study introduces ALGA-DenseNet, an advanced AI model for automatic ground-based cloud recognition. It achieves high accuracy, improving safety for Unmanned Aerial Vehicles (UAVs) by better identifying diverse cloud features.

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Area of Science:

  • Meteorology
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automatic cloud recognition is vital for meteorology and Unmanned Aerial Vehicle (UAV) safety.
  • Challenges include variable cloud shapes, lighting, and background interference.

Purpose of the Study:

  • To develop an improved DenseNet model for accurate ground-based cloud recognition.
  • To enhance feature extraction and model robustness for complex cloud imagery.

Main Methods:

  • Introduced ALGA-DenseNet, incorporating a multi-scale attention mechanism.
  • Utilized Color Jitter for image robustness and Adaptive Local and Global Attention (ALGA) for feature merging.
  • Integrated mixed and depthwise separable convolutions, Vision Transformer (ViT), and Dynamic Multi-head Attention (DMA).

Main Results:

  • Achieved 97.94% accuracy on the TJNU Ground-based Cloud Dataset (GCD).
  • Achieved 97.25% accuracy on the Cirrus Cumulus Stratus Nimbus (CCSN) dataset.
  • Demonstrated capability for fine-grained, multi-scale extraction of cloud textures, shapes, and colors.

Conclusions:

  • ALGA-DenseNet shows strong generalization performance for cloud recognition.
  • The model effectively addresses challenges in automatic cloud identification.
  • This advancement supports meteorological applications and UAV operational safety.