Detection of Small Floating Target on Sea Surface Based on Gramian Angular Field and Improved EfficientNet

Abstract

In order to exploit the advantages of CNN models in the detection of small floating targets on the sea surface, this paper proposes a new framework for encoding radar echo Doppler spectral sequences into images and explores two different ways of encoding time series: Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF). To emphasize the importance of the location of texture information in the GAF-encoded map, this paper introduces the coordinate attention (CA) mechanism into the mobile inverted bottleneck convolution (MBConv) structure in EfficientNet and optimizes the model convergence by the adaptive AdamW optimization algorithm. Finally, the improved EfficientNet model is used to train and test on the constructed GADF and GASF datasets, respectively. The experimental results demonstrate the effectiveness of the proposed algorithm. The recognition accuracy of the improved EfficientNet model reaches 96.13% and 96.28% on the GADF and GASF datasets, respectively, which is 1.74% and 2.06% higher than that that of the pre-improved network model. The number of parameters of the improved EfficientNet model is 5.38 M, which is 0.09 M higher than that of the pre-improved network model. Compared with the classical image classification algorithm, the proposed algorithm achieves higher accuracy and maintains lighter computation.