A dual-label-reversed ensemble transfer learning strategy for underwater target detection

Abstract

This paper proposed DLR (Dual-Label-Reversed) Ensemble Learning Strategy, a universal underwater acoustic target detection strategy with a transfer learning architecture, which can ensemble two transferred target detection models to enhance the detection accuracy. An acoustic data feature extraction strategy is employed to extract comprehensive features ranging from time/frequency domain to dedicated auditory parameters. A target detection model transfer strategy is proposed to get original transferred model and DLR transferred model from source domain to target domain. Then, the final detection can be made by the DLR ensemble learning strategy, which ensemble the output of two transferred model. We evaluate the proposed strategies using real underwater acoustic signal data. Experimental results show that the proposed algorithm can achieve a detection accuracy comparable with that trained with 2000 samples using only 200 labeled samples.