基于深度学习的水下目标识别算法研究

Research on Deep Learning Based Underwater Target Recognition Algorithms

摘要:

随着海洋资源开发和国防安全需求的不断增长，水下目标识别技术的重要性日益凸显。系统地研究了基于深度学习的水下目标识别技术，分析了当前主流算法的性能特点及其改进方法。概述了水下目标识别面临的挑战，详细介绍了基于深度学习的两阶段（Two-Stage）算法和一阶段（One-Stage）算法；重点对比了 Faster R-CNN、YOLO 系列以及基于 Transformer 的目标检测算法在识别精度、实时性和鲁棒性等方面的表现；探讨了注意力机制和多尺度特征融合技术在水下目标识别中的应用，这些技术能够有效提高模型的泛化能力和识别效率；总结了不同算法在标准数据集以及自建水下数据集上的性能表现，并对未来的研究方向提出了展望。

Abstract:

With the continuous growth of the demand for marine resource development and national defense security，the importance of underwater target recognition technology has become increasingly prominent. In this paper，the underwater target recognition technology based on deep learning is systematically studied，the performance of the current mainstream algorithms is analyzed，and the improvement methods are put forward. Firstly，the challenges faced by underwater target recognition are summarized，and the Two-Stage algorithm and One-Stage algorithm based on deep learning are introduced in detail. The performance of algorithms such as Faster R-CNN，the YOLO series，and target detection algorithms based on Transformer are compared in terms of recognition accuracy，real-time performance，and robustness. In addition，the application of the attention mechanism and multi-scale feature fusion technology in underwater target recognition is discussed. These technologies can effectively improve the generalization ability and recognition efficiency of the model. Finally，the performance of different algorithms on standard datasets and self-built underwater datasets is summarized，and future research direction is proposed.