水力发电2024，Vol.50Issue(7)：101-105,5.
基于块分解和循环神经网络的海上升压站智能巡检目标时序配准算法
Intelligent Inspection Target Time-series Registration Algorithm for Offshore Booster Stations Based on Block Decomposition and Recurrent Neural Networks
杨林刚 1王增光 2马润泽 1陈永春 2周才全1
作者信息
- 1. 中国电建集团华东勘测设计研究院有限公司,浙江 杭州 310000
- 2. 中国航空工业集团公司洛阳电光设备研究所,河南 洛阳 471000
- 折叠
摘要
Abstract
The timing registration of intelligent inspection targets for conventional offshore booster stations mainly relies on triangular grid representation methods,but it lacks analysis of the energy distribution of image blocks,resulting in a large proportion of outliers after target registration and poor registration results.To this end,a time-series registration algorithm for intelligent inspection targets of offshore booster stations based on block decomposition and recurrent neural networks is proposed.By analyzing the inspection items of offshore booster stations,the block decomposition algorithm is used to decompose the inspection images,detect the feature points of inspection targets,and extract the high-frequency components in the decomposed image blocks to obtain the energy distribution of images.Combined with the main curvature of target feature points,the scalar scaling coefficient of feature points is calculated,and compared with a given threshold to identify unstable extreme points and remove them.Based on this,a recurrent neural network is introduced to obtain the standard deviation of pixels between the centroid of inspection target point and the feature points,in order to identify the center point of the array for registration.Taking a practical offshore booster station project as the research background,the performance of the proposed method is verified.The results of the example show that,after using the proposed algorithm for temporal registration of inspection targets,the proportion of outliers is smaller and the registration effect is better.关键词
块分解/循环神经网络/巡检目标/目标配准/配准算法/海上升压站Key words
block decomposition/recurrent neural network/inspection target/target registration/registration algorithm/offshore booster station分类
信息技术与安全科学引用本文复制引用
杨林刚,王增光,马润泽,陈永春,周才全..基于块分解和循环神经网络的海上升压站智能巡检目标时序配准算法[J].水力发电,2024,50(7):101-105,5.
