Citation: Huang, H.; Zuo, Z.; Sun, B.;
Wu, P.; Zhang, J. Attentive SOLO for
Sonar Target Segmentation.
Electronics 2022, 11, 2904. https://
doi.org/10.3390/electronics11182904
Academic Editor: Silvia Liberata Ullo
Received: 5 August 2022
Accepted: 8 September 2022
Published: 13 September 2022
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Article
Attentive SOLO for Sonar Target Segmentation
Honghe Huang, Zhen Zuo *, Bei Sun, Peng Wu and Jiaju Zhang
College of Intelligence Science and Technology, National University of Defense Technology,
Changsha 410073, China
* Correspondence: z.zuo@nudt.edu.cn
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.
Keywords:
sonar target segmentation; location category; pyramid segmentation attention module;
gated fusion; sonar target segmentation dataset
1. Introduction
As countries pay more attention to the ocean environment, marine exploration plays
an important role in the field of marine research. Moreover, the growing demand for marine
surveys has greatly promoted the development of imaging sonar systems, which can be
carried by a surveying ship, USV and UUV to implement locate, identify and
tracking tasks
.
The sonar system obtains images based on the calculating process of transmitting
and recovering sound waves, in which the transmitted sound wave will be reflected back
and received after encountering the target object. Therefore, the received echo contains
the significant sound wave absorption characteristics of different objects. However, the
performance of the sonar system is constrained by the limitations of natural unstructured
terrain. Due to the complexity of an underwater acoustic channel and the variability in
sound wave scattering, the received echo is also mixed with interference, including envi-
ronmental noise, reverberation and sonar self-noise, which present significant challenges to
the accurate target segmentation of sonar images.
Over the past few decades, researchers have proposed various traditional sonar image
segmentation methods, including geometric features, probability models, level sets and
Markov random field (MRF) theory. Chen et al. [
1
] established a new energy function
combining unified MRF and level sets. Unified MRF is used for integrating the pixel-
level and region-level information to analyze inter-pixel and inter-region neighborhood
relationships. Further, LS evolved according to the results of UMRF, so that the model
can accurately segment sonar images. Ye et al. [
2
] proposed two new level sets for sonar
image segmentation. Firstly, the local texture feature is extracted by using a Gauss–Markov
random field model and integrated into the level set energy function to dynamically select
the region of interest. Then, the proposed two-phase and multi-phase level set models
are obtained by optimizing the energy function. Finally, the segmentation results are
Electronics 2022, 11, 2904. https://doi.org/10.3390/electronics11182904 https://www.mdpi.com/journal/electronics
