文献类型：期刊文献

英文题名：Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

作者：Zou, Lilan[1];Liang, Bo[1];Cheng, Xu[2];Li, Shufa[1];Lin, Cong[1]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Smart Innovat Norway, Hakon Melbergs Vei 16, N-1783 Halden, Norway

年份：2023

卷号：137

期号：3

起止页码：2641

外文期刊名：CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES

收录：SCI-EXPANDED(收录号：WOS:001035375100001)、、EI(收录号：20233814755773)、Scopus(收录号：2-s2.0-85171546434)、WOS

基金：Funding Statement: This work is supported by National Natural Science Foundation of China (Grant: 62272109) .

语种：英文

外文关键词：Underwater communication; intelligent sensor network; target detection; weighted frame fusion; shadow information

外文摘要：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.