基于深度学习的水下目标识别算法研究

Deep Learning-Based Underwater Target Recognition: Methodologies and Applications

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。本文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。首先,本文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Two-Stage算法和One-Stage算法。本文重点对比了Faster R-CNN、YOLO系列以及基于Transformer的目标检测算法(如DETR及其改进算法)在识别精度、实时性和鲁棒性等方面的表现。此外,本文还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。最后,本文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。

关键词:深度学习;Faster R-CNN;YOLO;Transformer

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. This paper systematically studies the underwater target recognition technology based on deep learning, and analyzes the performance characteristics of the current mainstream algorithms and their improvement methods. Firstly, this paper outlines the challenges faced by underwater target recognition, and provides a detailed introduction to the Two-Stage algorithm and One-Stage algorithm based on deep learning. This paper focuses on comparing the performance of algorithms such as Faster R-CNN, the YOLO series, and target detection algorithms based on Transformer (such as DETR and its improved algorithms) in terms of recognition accuracy, real-time performance, and robustness. In addition, this paper also explores the application of the attention mechanism and multi-scale feature fusion technology in underwater target recognition. These technologies can effectively improve the generalization ability and recognition efficiency of the model. Finally, this paper summarizes the performance of different algorithms on standard datasets and self-built underwater datasets, and puts forward prospects for future research directions.

Key words:deep learning; Faster R-CNN; YOLO; Transformer