Using Machine Learning Techniques to Detect Defects in Images of Metal Structures

被引：1

作者：

Dementev, V. E. ^{[1
]}

Suetin, M. N. ^{[1
]}

Gaponova, M. A. ^{[1
]}

机构：

[1] Ulyanovsk State Tech Univ, Ulyanovsk 432027, Russia

来源：

PATTERN RECOGNITION AND IMAGE ANALYSIS | 2021年 / 31卷 / 03期

基金：

俄罗斯基础研究基金会;

关键词：

unmanned aerial vehicles; deep learning; neural networks; signal processing systems;

D O I：

10.1134/S1054661821030068

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper is devoted to studying the capabilities of modern neural networks in image processing for solving the problem of monitoring the state of steel and reinforced concrete structures. The article presents a method for solving monitoring problems based on the use of a combination of several neural networks focused on recognizing a fragment of a structure and parts of a structure. Methods for training neural networks on small training samples are proposed. The results of the operation of the algorithms on real images are presented, showing the consistency and efficiency of the proposed solution.

引用

页码：506 / 512

页数：7

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