Open Access
ARTICLE
Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network
Lilan Zou1, Bo Liang1, Xu Cheng2, Shufa Li1,*, Cong Lin1,*
1
College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China
2
Smart Innovation Norway, Hakon Melbergs vei 16, Halden, 1783, Norway
* Corresponding Authors: Shufa Li. Email: ; Cong Lin. Email:
(This article belongs to the Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
Computer Modeling in Engineering & Sciences 2023, 137(3), 2641-2659. https://doi.org/10.32604/cmes.2023.028037
Received 27 November 2022; Accepted 07 March 2023; Issue published 03 August 2023
Abstract
Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater
environment. In order to solve the problem that some semantic information in sonar images is lost and model
detection performance is degraded due to the complex imaging environment, we proposed a more effective and
robust target detection framework based on deep learning, which can make full use of the acoustic shadow
information in the forward-looking sonar images to assist underwater target detection. Firstly, the weighted box
fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence, so
as to obtain accurate acoustic shadow boxes. Further, the acoustic shadow box is cut down to get the feature map
containing the acoustic shadow information, and then the acoustic shadow feature map and the target information
feature map are adaptively fused to make full use of the acoustic shadow feature information. In addition, we
introduce a threshold processing module to improve the attention of the model to important feature information.
Through the underwater sonar dataset provided by Pengcheng Laboratory, the proposed method improved the
average accuracy by 3.14% at the IoU threshold of 0.7, which is better than the current traditional target detection
model.
Graphical Abstract
Keywords
Supplementary Material
Supplementary Material File
Cite This Article
Zou, L., Liang, B., Cheng, X., Li, S., Lin, C. (2023). Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network.
CMES-Computer Modeling in Engineering & Sciences, 137(3), 2641–2659. https://doi.org/10.32604/cmes.2023.028037