A Survey on Noise Reduction and Target Detection in Forward-looking Sonar Images
With the continuous development of underwater environment perception technologies,acoustic-based sens-ing methods have gradually become mainstream,benefiting from the long propagation distance and wide coverage of sound waves in water.Among various acoustic sensing technologies,forward-looking sonar,with its ability to detect objects in the field of view in real-time,plays a crucial role in underwater environment perception and has been widely applied in fields such as fisheries,maritime safety,and military operations.However,the performance of forward-looking sonar is limited by the acoustic propagation characteristics and interference from the complex underwater environment.Its high noise and low signal-to-noise ratio data present significant challenges for sonar imaging and target detection.While traditional sonar image denoising methods have been extensively validated and applied in practical scenarios,deep learning-based sonar image de-noising technologies have shown more prominent advantages in dealing with the complex noise found in forward-looking so-nar data.The field of forward-looking sonar target detection has undergone a revolutionary shift from traditional algorithms to deep learning methods,significantly improving detection accuracy and generalization capabilities.This paper reviews the development of sonar image denoising and target detection in both traditional and deep learning methods,systematically summarizes current research progress and methodologies,and highlights emerging innovations based on deep learning.It al-so analyzes the prospects for application in complex underwater environments and discusses potential future research direc-tions,including data fusion,algorithm optimization,and challenges in real-world applications.
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