基于机器视觉的填埋场防渗层破损识别方法

陈亚宇; 李建龙; 孙骥晟; 王宏达; 毕士君

doi:10.13205/j.hjgc.202108019

基于机器视觉的填埋场防渗层破损识别方法

doi: 10.13205/j.hjgc.202108019

1. 河北工程大学机械与装备工程学院, 河北邯郸 056038;
2. 河北维立方科技有限公司, 河北唐山 063000

基金项目:

国家自然科学基金项目（51807047）；河北省省级科技计划资助（20373901D）；河北省普通高等学校科学技术研究项目资助（BJ2019003）。

详细信息

作者简介:
陈亚宇(1984-),男,副教授,博士,主要研究方向为过程检测与控制。121847157@qq.com

通讯作者:
李建龙(1996-),男,硕士,主要研究方向为过程检测与控制,机器视觉。1639591683@qq.com

计量
- 文章访问数: 125
- HTML全文浏览量: 13
- PDF下载量: 8
- 被引次数: 0
出版历程
- 收稿日期: 2020-11-14
- 网络出版日期: 2022-01-18

RESEARCH ON THE DAMAGE RECOGNIZING METHOD OF IMPERVIOUS LAYER OF LANDFILL BASED ON MACHINE VISION

1. College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China;
2. Hebei Weicube Technology Co., Ltd, Tangshan 063000, China

摘要

摘要: 填埋场防渗层高密度聚乙烯（HDPE）膜在运营中极易破损，运用在线监测技术确定渗漏区域，将该区域膜上介质移除后，需对漏洞边缘进行精确识别，为实现智能焊接提供视觉基础。因此，提出一种基于机器视觉的填埋场防渗层破损识别方法。首先对样本集进行图像处理，包括图像灰度化、高斯滤波除噪、点运算增强、阈值法分割以及数学形态学处理；其次根据图像的形态特征提取连通域数量、破损面积、周长、长轴、短轴以及轴比，采用holdout方法将样本集划分为训练集与测试集，并将提取到的特征作为输入量，对SVM进行训练；最后采用多个SVM进行分类识别。经实验验证，分类器的总体识别准确率为98.33%，其中块状破损识别准确率为98.24%，缝式破损为98.42%。
- 填埋场防渗层 /
- 机器视觉 /
- 图像处理 /
- SVM /
- 破损识别
Abstract: The high-density polyethylene (HDPE) film of the anti-seepage layer of the landfill is easily damaged during operation. The online monitoring technology is used to determine the leakage area. After the medium on the membrane removed, the loopholes need to be accurately identified to provide a visual basis for welding process. Therefore, a machine vision-based damage identification method for impermeable layer in landfill was proposed. First, perform image processing on the sample set, including image grayscale, Gaussian filter denoising, point operation enhancement, threshold segmentation, and mathematical morphology processing. Secondly, the number of connected domains, damage area, circumference, major axis, minor axis and axial ratio were extracted according to the morphological features of the image. The retention method weas used to divide the sample set into a training set and a test set, and then the extracted features were used as the input for training SVM. Finally, multiple SVMs were used for classification and recognition. Experiments showed that the overall recognition accuracy of the classifier was 98.33%, among which the accuracy of block damage recognition was 98.24%, and the stitch damage was 98.42%.
- landfill impervious layer /
- machine vision /
- image processing /
- SVM /
- damage identification

HTML全文

参考文献(20)

[1]	吁思颖.我国固体废物处理处置产业发展现状及趋势[J].清洗世界,2019,35(11):73-74.
[2]	傅刚辉.HDPE膜在垃圾处理中心的施工技术[J].建筑技术,2017,48(11):1208-1210.
[3]	ROWE R K,YAN Y.Magnitude and significance of tensile strain in geomembrane landfill liners[J].Geotextiles and Geomembranes,2019,47(3):439-458.
[4]	SUN X C,XU Y,LIU Y Q,et al.Evolution of geomembrane degradation and defects in a landfill:impacts on long-term leachate leakage and groundwater quality[J].Journal of Cleaner Production,2019,224:335-345.
[5]	HAN Z Y,MA H N,SHI G Z,et al.A review of groundwater contamination near municipal solid waste landfill sites in China[J].Science of the Total Environment,2016,569/570:1255-1264.
[6]	徐亚,能昌信,刘玉强,等.基于环境风险的危险废物填埋场安全寿命周期评价[J].中国环境科学,2016,36(6):1802-1809.
[7]	杨帆.城市固体废物的渗滤液处理与处置研究[J].节能与环保,2020,22(7):82-83.
[8]	杨坪,姜涛,李志成,等.填埋场防渗处理及渗漏检测方法研究进展[J].环境工程,2017,35(11):129-132 ,142.
[9]	陈亚宇,杨家良,孙焕奕,等.固体废弃物填埋场传输线法渗漏检测定位研究[J].环境工程,2018,36(6):128-133 ,154.
[10]	PANDEY L M S,SANJAY K S.An insight into waste management in Australia with a focus on landfill technology and liner leak detection[J].Journal of Cleaner Production,2019,225:1147-1154.
[11]	陈亚宇,能昌信,王振翀,等.基于传输线模型的垃圾填埋场渗漏定位方法探讨[J].煤炭工程,2012(3):105-107.
[12]	杨荣,王明伟,刘思铭.基于图像处理算法的目标识别、定位与跟踪系统设计与实现[J].物联网技术,2020,10(9):75-79.
[13]	王燕妮,贺莉.基于多分类SVM的石榴叶片病害检测方法[J].计算机测量与控制,2020,28(9):191-195.
[14]	梁璠,赵冬青,储成群,等.自适应灰度多段线性变换的FPGA实现[J].电子设计工程,2020,28(2):134-138.
[15]	崔欣,张鹏,赵静,等.基于机器视觉的玉米种粒破损识别方法研究[J].农机化研究,2019,41(2):28-33 ,84.
[16]	DAS K,BEHERA R N.A survey on machine learning:concept,algorithms and applications[J].International Journal of Innovative Research in Computer and Communication Engineering,2017,5(2):1301-1309.
[17]	WANG S,XU J F,WANG F Z,et al.Identification and detection of surface defects of outer package printed matter based on machine vision[J].Journal of Korea Technical Association of the Pulp and Paper Industry,2020,52(2):3-11.
[18]	BAO G J,JIA M M,XUN Y,et al.Cracked egg recognition based on machine vision[J].Computers and Electronics in Agriculture,2019,158(3):159-166.
[19]	YANG N,QIAN Y,ZHANG R B,et al.Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method[J].Journal of the Science of Food and Agriculture,2019,99(14):6589-6600.
[20]	HABIB M T,MAINMDER A,MORIUM A,et al.Machine vision based papaya disease recognition[J].Journal of King Saud University-Computer and Information Sciences,2020,32(3):300-309.