The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.

中文翻译：

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基于深度学习框架的水下目标识别方法综述

自主水下航行器（AUV）水下目标识别的准确性是水下探测、救援和安全的有力保障。近年来，深度学习在目标识别和分类的数字图像处理方面取得了重大进展，使得水下目标识别研究成为一个热门研究领域。本文系统地介绍了近几年深度学习在水下图像分析中的应用，并简要阐述了各种水下目标识别方法的基本原理。同时指出了各种方法的适用条件和优缺点。分析了AUV水下危险目标识别方法存在的技术问题，并给出了相应的解决方案。同时，