Sea surface small target detection based on convolutional attention

It is a challenging task to be able to quickly detect tiny targets on sea surface under a complex marine environment with strong clutter. Because the difference in statistical properties of clutter varies greatly, traditional target detection methods suffer from feature extraction difficulties. In this paper, a convolutional attention-based method for detecting small targets on the sea surface is proposed. Radar echo datasets are first preprocessed to obtain time-frequency distribution spectrogram information, which is transformed into feature images for the input of network. Then a multi-scale convolutional attention fusion (MCAF) module is designed to enhance feature representation while reducing the interference of clutter in a lightweight manner without bells and whistles. Our method results on the IPIX dataset show that detection accuracy can achieve 88.9% under lower signal-to-clutter ratio (SCR) sea states when the false alarm rate is 10^-3. Compared with other detection models, our proposed network model has stronger detection performance.