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FDA-DETR: A frequency-aware DETR with dynamic query and adaptive multi-task optimization for oriented small object

Cheng Ju1,2, Yu Zhao1,2, Shuiqing Miao1,2

  • 1School of Data Science and Engineering, Xi'an Innovation College of Yan'an University, Xi'an, China.

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Processing

Background:

  • Oriented small object detection is difficult due to weak features and high computational costs in existing DETR models.
  • Small objects require amplification of high-frequency details for effective discrimination.

Purpose of the Study:

  • To introduce Frequency Domain Awareness Detection Transformer (FDA-DETR) for enhanced accuracy and efficiency in oriented small object detection.
  • To address limitations of current DETR-based detectors in handling small and densely distributed objects.

Main Methods:

  • Developed a multi-scale frequency domain enhancement to amplify crucial high-frequency details.
  • Introduced a density-aware dynamic query mechanism for adaptive resource allocation.
  • Incorporated multi-granularity attention fusion and adaptive multi-task loss based on Bayesian uncertainty.

Main Results:

  • FDA-DETR demonstrated superior detection accuracy and faster inference speeds compared to existing DETR methods.
  • Significant improvements were observed for detecting small and densely distributed objects.
  • Ablation studies and theoretical analysis validated the effectiveness of the proposed modules.

Conclusions:

  • FDA-DETR provides a robust and efficient solution for oriented small object detection.
  • The framework offers new insights into feature learning and attention mechanisms for computer vision tasks.