AdaRW training optimization algorithm for deep learning model of marine target detection based on SAR

ABSTRACT

Deep learning adjusts parameters to optimize the model through model training. Training algorithm is the key to model optimization and implementation. Therefore, the improvement of model training algorithm is of great significance to deep learning. Based on the original gradient algorithm, the paper proposes a new gradient descent algorithm AdaGrad Restricted by Windows (AdaRW) for Deep learning model training optimization. Aiming at the defects of AdaGrad algorithm, the new algorithm uses the subset of window to limit historical accumulation, so as to slow down the attenuation of learning rate, and improve the speed of model training. The paper constructs OceanTDA9, a Deep learning model of marine target detection for Synthetic Aperture Radar (SAR) data, and adopts the proposed AdaRW algorithm to train the model based on SAR data with a resolution of 10 m in the Bohai Sea. Experiment shows that the accuracy and loss of the algorithm are better than those of AdaGrad and Stochastic Gradient Descent (SGD) algorithms, and the standard deviation is better than that of Adam algorithm.

Keywords:

• Marine target detection
• Deep learning model
• Gradient descent algorithm
• AdaGrad algorithm

Acknowledgements

This study was supported by data from European Space Agency (ESA) and the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences.

Disclosure statement

No potential conflict of interest was reportedby the authors.

Data availability statement

The code and data for this research is availa ble at ‘http://doi.org/10.5281/zenodo.5055163’

Additional information

Funding

The study was supported by the Natural Science Foundation of Shandong Province (No. ZR 2019MD034)