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Split_ Composite: A Radar Target Recognition Method on FFT Convolution Acceleration.

Xuanchao Li1, Yonghua He2, Weigang Zhu2

  • 1Graduate School, Space Engineering University, Beijing 101416, China.

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|July 27, 2024
PubMed
Summary
This summary is machine-generated.

A new Split_ Composite method accelerates ship target recognition using Synthetic Aperture Radar (SAR) by optimizing frequency-domain convolution. This technique enhances inference speed and efficiency for large-scale data processing without sacrificing accuracy.

Keywords:
CNNscomposite zero-paddingfast Fourier transforminference speedinput block decompositionship target recognition

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Area of Science:

  • Remote Sensing
  • Artificial Intelligence
  • Signal Processing

Background:

  • Synthetic Aperture Radar (SAR) offers all-weather, all-time imaging crucial for ship target recognition.
  • Deep learning models, particularly Convolutional Neural Networks (CNNs), face efficiency challenges in the frequency domain due to memory and real-time constraints in embedded systems.

Purpose of the Study:

  • To introduce an innovative convolution acceleration technique, Split_ Composite, designed to overcome the limitations of CNNs in frequency-domain processing for SAR ship target recognition.
  • To enhance the inference velocity and scalability of SAR ship target recognition systems.

Main Methods:

  • The Split_ Composite method utilizes Fast Fourier Transform (FFT) for convolution acceleration.
  • It employs input block decomposition and composite zero-padding to optimize memory bandwidth and computational complexity.
  • Frequency-domain convolution and image reconstruction are streamlined, leveraging FFT periodicity for improved frequency resolution and weight sharing.

Main Results:

  • Experiments on the OpenSARShip-4 dataset demonstrate that Split_ Composite maintains high recognition precision.
  • The method significantly enhances inference velocity, particularly for large-scale SAR data processing.
  • Split_ Composite exhibits superior scalability and efficiency compared to existing methods like Winograd and TensorRT.

Conclusions:

  • The Split_ Composite method effectively accelerates SAR ship target recognition by optimizing frequency-domain operations.
  • This technique provides a scalable and efficient solution for real-time embedded systems without compromising recognition accuracy.
  • Split_ Composite represents a significant advancement in SAR image analysis and deep learning acceleration.