摘要
图像识别作为人工智能的关键技术之一,被广泛应用于各个行业,水利数字化、智能化的发展离不开人工智能图像识别技术的应用。首先概述图像识别技术的发展过程,再详细介绍传统的图像识别技术(统计法、句法识别法和几何变换法),以及引入神经网络算法的图像识别技术(深度置信网络、卷积神经网络、循环神经网络和图卷积神经网络),并对这些技术的优缺点进行比较,最后从河道流速测量、遥感水体识别、水位监测和三维预测4个方面综述人工智能图像识别在水利行业的应用进展,提出提高技术场景适配能力、兼容性和识别精度等将是人工智能图像识别在水利行业应用的发展方向。
As one of the key technologies of artificial intelligence,image recognition has been widely used in various industries.The develop⁃ment of digital and intelligent water conservancy cannot be separated from the application of artificial intelligence image recognition technolo⁃gy.This paper firstly summarized the development and recognition process of image recognition technology,introduced the traditional image recognition technology,including statistical method,syntactic recognition method and geometric transformation method,and the image recog⁃nition technology with neural network algorithm,including depth confidence network,convolution neural network,cyclic neural network and graph convolution neural network and compared the advantages and disadvantages of the above image recognition technologies.This paper summarized the application of artificial intelligence image recognition in water conservancy industry from the four aspects of river velocity measurement,remote sensing water body recognition,water level monitoring and three-dimensional prediction.Finally,It was proposed that improving the adaptability,compatibility and recognition accuracy of technical scenarios would be the development direction of artificial intel⁃ligence image recognition in the water conservancy industry.
作者
李涛
徐高
梁思涵
李英睿
王敏
李冰
LI Tao;XU Gao;LIANG Sihan;LI Yingrui;WANG Min;LI Bing(Yellow River Institute of Hydraulic Research,YRCC,Zhengzhou 450003,China;Wuhu Water Affairs Bureau,Wuhu 241000,China)
出处
《人民黄河》
CAS
北大核心
2022年第11期163-168,共6页
Yellow River
基金
河南省杰出青年基金资助项目(202300410539)
河南省优秀青年科学基金资助项目(212300410059)
河南省重大科技专项(201400211000)
中央级公益性科研院所基本科研业务费专项(HKY-JBYW-2020-21,HKY-JBYW-2020-07,HKYJBYW-2022-11)。
关键词
人工智能
图像识别
深度学习
神经网络
水利行业
artificial intelligence
image identification
deep learning
neural network
water conservancy industry