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TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer.

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  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410082, China.

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This study introduces TransEffiDet, an improved aircraft detection method for aerial images. It enhances object detection accuracy by combining EfficientDet with Transformer modules for better feature analysis.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Aircraft detection in aerial imagery is crucial for military applications but faces challenges like poor environmental conditions and vast backgrounds.
  • Existing methods struggle with accuracy and robustness in complex aerial scenes.

Purpose of the Study:

  • To propose a novel and robust aircraft detection method for aerial images.
  • To improve the accuracy and efficiency of aircraft detection by leveraging deep learning architectures.

Main Methods:

  • The proposed TransEffiDet method integrates the EfficientDet architecture with a Transformer module.
  • EfficientDet serves as the backbone for feature fusion, while a deformable Transformer analyzes long-range dependencies.
  • A custom fusion module combines short-range and long-range features for enhanced representation.

Main Results:

  • TransEffiDet achieved a mean Average Precision (mAP) of 86.6%, outperforming the baseline EfficientDet by 5.8%.
  • The method demonstrated superior robustness compared to other existing detection techniques.
  • A new public aerial dataset for aircraft detection has been established.

Conclusions:

  • TransEffiDet offers a significant advancement in aerial aircraft detection accuracy and robustness.
  • The integration of Transformer modules effectively addresses challenges posed by complex aerial environments.
  • The release of a new public dataset will facilitate further research in this domain.