IS3L: An Integrated Self-training Semi-Supervised Learning Strategy for Underwater Acoustic Target Detection

Abstract

This paper proposed IS3L (Integrated Self-training Semi-Supervised Learning), a universal underwater acoustic target detection strategy with a semi-supervised learning architecture, which can comprehensively refine weakly labeled samples and label samples automatically to boost the detection accuracy. To our best knowledge, this is the first work to combine semi-supervised learning with underwater target detection. A universal feature extraction strategy is employed to extract comprehensive features ranging from time/frequent domain to dedicated auditory parameters. An integrated self-training strategy is proposed to generate pseudo labels from unlabeled samples and meanwhile avoid wrong labeling via a voting mechanism. Then, the final detection can be made by the rear classifier, which has been fully trained with original labeled samples and generated pseudo-labeled samples. Experimental results, which are based on real sea trail data, show that the proposed algorithm can reach up to 98.13% in detection accuracy with only 50 labeled samples (7040 samples in total).