基于改进活动轮廓模型的井筒病害识别方法

doi:10.11799/ce201605036

煤炭工程 ›› 2016, Vol. 48 ›› Issue (5): 115-118.doi: 10.11799/ce201605036

基于改进活动轮廓模型的井筒病害识别方法

岳国伟¹,卢秀山¹,刘冰²,刘如飞²

1. 山东科技大学
2. 山东科技大学测绘学院

收稿日期:2016-02-01 修回日期:2016-02-18 出版日期:2016-05-10 发布日期:2016-06-15
通讯作者: 刘冰 E-mail:13780686818@163.com

A method of mine shaft disease recognition based on improved active contour model

Received:2016-02-01 Revised:2016-02-18 Online:2016-05-10 Published:2016-06-15

摘要/Abstract

摘要： 针对井筒图像对比度小、干扰噪声多、分割精度不高、识别效率低等问题，提出了一种基于图像增强与正则约束的改进活动轮廓模型。图像增强算子能够改善井筒图像的对比度，扩大灰度动态范围，抑制噪声干扰。正则约束因子，包括长度约束因子、面积约束因子和距离函数约束因子，能够减少初始轮廓对曲线演化的影响，实现轮廓曲线平滑快速向目标边界移动，并最终与目标边缘吻合。实验结果表明，本文模型在算法性能和分割效果上都优于C-V模型和LBF模型，能够快速准确识别井筒病害，提高井筒巡检的自动化程度。

关键词: 活动轮廓模型, 图像增强, 正则约束因子, 井筒图像, 病害识别

Abstract: In the images of mine shaft, there are many problems such as the low image contrast, the more image noises and the low efficiency of image segmentation accuracy. In order to solve these problems, we propose an improved active contour model based on image enhancement and regularization constraint. The image enhancement can improve the image contrast, expand the dynamic range of gray value, and reduce the noise effect. The regular constraints include the length constraint, the area constraint and the distance function constraint. They can reduce the influence of initial contour on the evolution of the curve, realize the contour curve to move quickly to the target boundary, and end up with the edge of the target. Experimental results show that the proposed model is superior to the C-V model and LBF model in the performance and segmentation results. This model can recognize the mine shaft diseases quickly and accurately, which can improve the degree of automation of mine shaft inspection.

中图分类号:

TD679

岳国伟，卢秀山，刘冰，刘如飞. 基于改进活动轮廓模型的井筒病害识别方法[J]. 煤炭工程, 2016, 48(5): 115-118.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://www.coale.com.cn/CN/10.11799/ce201605036

http://www.coale.com.cn/CN/Y2016/V48/I5/115

参考文献

[1]. 刘环宇,王思敬,曾钱帮,等. 基于模糊神经网络兖州矿区竖井井筒非采动破裂的判别[J]. 岩土工程学报, 2005, 27:1237-1240.
[2] 周晓敏, 胡启胜, 马成炫,等. 围岩对竖井井壁承载能力影响的对比研究[J]. 煤炭学报, 2012.
[3] Yu Q, Ikuta S, Shimada H, et al. Application of Underground Continuous Impervious Curtain to Shaft Lining Rupture Treatment in Eastern Chinese Coal Mines[J]. Memoirs of the Faculty of Engineering, 2012, 72(2): 39-53.
[4] Dubrovina A, Hershkovitz R, Kimmel R. Image editing using level set trees[C]// Image Processing (ICIP), 2014 IEEE International Conference onIEEE, 2014:4442 - 4446.
[5] Kass M, Witkin A, Terzopoulos D. Snakes: Active Contour Models[J].International Journal of Computer Vision, 1987 : 321-331.
[6]Bashir A D, Andrew H, David S. An active contour model for segmenting and measuring retinal vessels.[J]. Medical Imaging IEEE Transactions on, 2009, 28(9):1488-97.
[7] Chan T F, Vese L A. Active contours without edges.[J]. Image Processing IEEE Transactions on, 2001, 10(2):266 - 277.
[8] Chunming Li, Chiu-yen Kao, John C. Gore, et al. Implicit Active Contours Driven by Local Binary Fitting Energy[C] Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference onIEEE, 2007:1-7.
[9] 朱晓舒,孙权森,夏德深,等. 基于核函数的活动轮廓模型[J]. 计算机辅助设计与图形学学报, 2015, 27:388-393.
[10] 孙传猛,曹树刚,李勇. 基于LBF改进模型的煤岩细观结构研究[J]. 煤炭学报, 2015, 40(02):331-341.
[11] Abdelsamea M M, Gnecco G, Gaber M M. An efficient Self-Organizing Active Contour model for image segmentation[J]. Neurocomputing, 2015, 149:820-835.
[12] Rahman R, Kabir S M R, Quadir A. Application of fuzzy inference and active contour model for detection of fovea and its center in a fundus image[J]. Signal Image & Video Processing, 2015:1-8.
[13] Hashemi S, Kiani S, Noroozi N, et al. An image contrast enhancement method based on genetic algorithm[J]. Pattern Recognition Letters, 2010, 31(13):1816-1824.
[14] Sawant M, Deore M. A Comprehensive Review of Image Enhancement Techniques[J]. International Journal of Computer Technology & Electronics Engineering, 2011, 1(2)：39-44.
[15] Kropac O, Mucka P. Specification of Obstacles in the Longitudinal Road Profile by Median Filtering[J]. Journal of Transportation Engineering, 2014, 137(3):214-226.

基于改进活动轮廓模型的井筒病害识别方法

A method of mine shaft disease recognition based on improved active contour model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张士岭李明建. 普通钻孔智能设计系统研究与应用[J]. , 2019, 51(6): 0-0.
[2]	白璐，王然风. 基于RSSI井下人员定位Trilateration-GSA算法研究[J]. 煤炭工程, 2018, 50(12): 123-126.
[3]	梁玉柱梅益杨幸雨罗宁康. 煤矿井下探测作业机器人轨迹粒子群规划方法研究[J]. 煤炭工程, 2018, 50(9): 124-127.
[4]	薛光辉，张昊，王东，管健，蔡文静. 基于ZigBee技术无线传感器研制[J]. 煤炭工程, 2018, 50(8): 168-172.
[5]	吴畏, 唐丽均. 矿用井下智能交通控制系统设计[J]. 煤炭工程, 2018, 50(7): 10-13.
[6]	赵建伟，项远城，张冬顺，于鹏，杨思宇. 基于自适应算法驱动器的矿用机器人运动控制[J]. 煤炭工程, 2018, 50(7): 158-162.
[7]	孙哲星. 煤矿井下人员二维精确定位方法[J]. 煤炭工程, 2018, 50(5): 97-99.
[8]	刘振振, 王菲, 刘世伟, 高洁. 基于广义Ｓ变换的煤系断层槽波信号时频分析[J]. 煤炭工程, 2018, 50(4): 132-134.
[9]	郭涛,王思宁. 基于ZigBee+WiFi的液压支架压力监测系统设计[J]. 煤炭工程, 2018, 50(3): 149-151.
[10]	王莉，王雨，荆恬，等. 絮凝法处理煤炭地下气化废水试验研究[J]. 煤炭工程, 2017, 49(8): 20-23.
[11]	孙丁丁，尚仕波. 煤炭装车喷洒一体化的双喷洒系统设计[J]. 煤炭工程, 2017, 49(8): 59-62.
[12]	郭西进，邵辉，王广胜，陈晓天. 基于GA-BP神经网络的浮选加药量预测[J]. 煤炭工程, 2017, 49(2): 106-108.
[13]	王新文，韩阳，常圣强，宫三朋，杨伟. ZKX2448直线振动筛结构共振的双判定[J]. 煤炭工程, 2016, 48(10): 117-119.
[14]	孙社文，王瑛，林伟杰，等. LonWorks工业控制网络安全鉴别系统设计[J]. 煤炭工程, 2016, 48(5): 37-39.
[15]	张鹏. 基于改进粒子滤波的煤矿机器人定位方法研究[J]. 煤炭工程, 2016, 48(3): 113-115.