Attentive SOLO for Sonar Target Segmentation

Abstract

Imaging sonar systems play an important role in underwater target detection and location. Due to the influence of reverberation noise on imaging sonar systems, the task of sonar target segmentation is a challenging problem. In order to segment different types of targets in sonar images accurately, we proposed the gated fusion-pyramid segmentation attention (GF-PSA) module. Specifically, inspired by gated full fusion, we improved the pyramid segmentation attention (PSA) module by using gated fusion to reduce the noise interference during feature fusion and improve segmentation accuracy. Then, we improved the SOLOv2 (Segmenting Objects by Locations v2) algorithm with the proposed GF-PSA and named the improved algorithm Attentive SOLO. In addition, we constructed a sonar target segmentation dataset, named STSD, which contains 4000 real sonar images, covering eight object categories with a total of 7077 target annotations. The experimental results show that the segmentation accuracy of Attentive SOLO on STSD is as high as 74.1%, which is 3.7% higher than that of SOLOv2.