|本期目录/Table of Contents|

[1]董 杰,刘文峰,乔法起,等.基于SVM的井下水仓淤泥识别系统的研究[J].工业仪表与自动化装置,2021,(05):112-116.[doi:10.19950/j.cnki.cn61-1121/th.2021.05.024]
　DONG Jie,LIU Wenfeng,QIAO Faqi,et al.Research on sludge recognition system of underground water silo based on SVM[J].Industrial Instrumentation & Automation,2021,(05):112-116.[doi:10.19950/j.cnki.cn61-1121/th.2021.05.024]
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基于SVM的井下水仓淤泥识别系统的研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2021年05期
页码:
112-116
栏目:
出版日期:
2021-10-15

文章信息/Info

Title:
Research on sludge recognition system of underground water silo based on SVM
作者:
董 杰1; 刘文峰1; 乔法起1; 蔡佩征2; 侯力扬2; 孟祥忠2
1.内蒙古黄陶勒盖煤炭有限责任公司，内蒙古 鄂尔多斯 017212；
2.青岛科技大学，山东 青岛 266000
Author(s):
DONG Jie1; LIU Wenfeng1; QIAO Faqi1；CAI Peizheng2; HOU Liyang2; MENG Xiangzhong2
1.Inner Mongolia huangtaolegai Coal Co., Ltd., Inner Mongolia Ordos 017212,China;
2.Qingdao University of science and technology, Shandong Qingdao 266000,China
关键词:
水仓淤泥表面含水量识别; 机器视觉; SVM支持向量机; 图像识别
Keywords:
recognition of water content on silt surface of water silo;  machine vision;  SVM support vector machine;  image recognition
分类号:
TP2
DOI:
10.19950/j.cnki.cn61-1121/th.2021.05.024
文献标志码:
A
摘要:
针对煤矿井下水仓经常出现的淤泥中含水量过多而导致的涌仓问题，该文设计了一种基于SVM的井下水仓淤泥表面含水量识别系统。以井下水仓的淤泥与水面对光线反射的不同情况作为识别特征，通过对数及对比度拉伸变换法提高图片局部亮度；采用自适应阈值分割法将灰度图进行二值化处理。在SVM模型中进行前景、背景分割时，将二值化图片作为输入而不是像传统SVM分类直接输入原图，有利于提高识别精度。分别计算将彩色图、二值图作为输入图像的相似度、正确率、错分率和漏分率，得出结果：采用二值化图片作为输入图片的各项分割属性均优于彩色图作为输入图像，为井下水仓涌仓预警系统的搭建提供了基础。
Abstract:
Aiming at the problem of flooding caused by excessive water content in the silt that often occurs in underground sumps in coal mines, a SVM-based system for identifying the surface water content of silt in underground sumps is designed. Taking the silt in the underground sump and the reflection of light on the water surface as the identification feature, the local brightness of the image is improved by the logarithmic and contrast stretching transformation method; the adaptive threshold segmentation method is used to binarize the gray image. When segmenting the foreground and background in the SVM model, the binary image is used as input instead of directly inputting the original image like the traditional SVM classification, which is beneficial to improve the recognition accuracy. Calculate the similarity, correct rate, error rate and omission rate of the color image and the binary image as the input image. The result is that the segmentation attributes of the binary image as the input image are better than the color image As the input image, it provides the basis for the construction of the early warning system of underground water storage inrush.

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备注/Memo

备注/Memo:
收稿日期：2020-07-24

作者简介：
孟祥忠，工学博士，教授，主要研究方向为仪器与测试技术等。
更新日期/Last Update: 1900-01-01