| 朱真慧,汪雪良,孙函宇,姚骥.视觉测波技术及其图像智能优化算法研究进展[J].装备环境工程,2025,22(1):114-124. ZHU Zhenhui,WANG Xueliang,SUN Hanyu,YAO Ji.Research Progress on Visual Wave Measurement Technology and Intelligent Image Optimization Algorithm[J].Equipment Environmental Engineering,2025,22(1):114-124. |
| 视觉测波技术及其图像智能优化算法研究进展 |
| Research Progress on Visual Wave Measurement Technology and Intelligent Image Optimization Algorithm |
| 投稿时间：2024-10-30 修订日期：2024-12-05 |
| DOI：10.7643/issn.1672-9242.2025.01.012 |
| 中文关键词: 机器视觉 波浪监测 图像识别 特征匹配 神经网络 深度学习 智能算法中图分类号:TP391.4 文献标志码:A 文章编号:1672-9242(2025)01-0114-11 |
| 英文关键词:machine vision wave monitoring image recognition feature matching neural network deep learning intelligent algorithm |
| 基金项目:国家重点研发计划项目(2024YFE0104300) |
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| Author | Institution |
| ZHU Zhenhui | China Ship Scientific Research Center, Jiangsu Wuxi 214082, China |
| WANG Xueliang | China Ship Scientific Research Center, Jiangsu Wuxi 214082, China;National Key Laboratory of Ship Structural Safety, Jiangsu Wuxi 214082, China |
| SUN Hanyu | China Ship Scientific Research Center, Jiangsu Wuxi 214082, China;National Key Laboratory of Ship Structural Safety, Jiangsu Wuxi 214082, China |
| YAO Ji | China Ship Scientific Research Center, Jiangsu Wuxi 214082, China;National Key Laboratory of Ship Structural Safety, Jiangsu Wuxi 214082, China |
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| 中文摘要: |
| 综述了波浪监测中基于视觉的测波技术，重点讨论了单目、双目、多目视觉技术的基本原理，并通过应用案例分析其优势与不足。基于智能算法正逐渐应用到图像处理中，如卷积神经网络、长短期记忆神经网络等，对人工智能在视觉测波技术中角点检测、特征匹配等阶段的具体应用进行了分析，讨论了深度学习算法在波浪反演、波浪预测中取得的进展，阐述了智能算法及技术对优化波浪监测系统的重要性，并展望了未来人工智能在此领域的发展趋势。 |
| 英文摘要: |
| The work aims to provide an overview of vision-based wave measurement technology in wave monitoring, focusing on the basic principles of monocular, binocular, and multiocular vision technologies and analyze their advantages and disadvantages through application case studies. Intelligent algorithms, such as convolutional neural networks and long short-term memory neural networks, are gradually being applied to image processing. The specific applications of artificial intelligence in stages such as corner detection and feature matching in visual wave measurement technology are analyzed. The progress made by deep learning algorithms in wave inversion and wave prediction is discussed. The importance of intelligent algorithms and technologies in optimizing wave monitoring systems is elaborated, and the future development trends of artificial intelligence in this field are prospected. |
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