Research on defect inspection method of pipeline robot based on adaptive image enhancement

Li Ping; Liang Dan; Liang Dongtai; Wu Xiaocheng; Chen Xing

doi:10.12086/oee.2020.190304

Article navigation > Opto-Electronic Engineering > 2020 Vol. 47 > No. 1 > 190304

Next Article Previous Article

Li P, Liang D, Liang D T, et al. Research on defect inspection method of pipeline robot based on adaptive image enhancement[J]. Opto-Electron Eng, 2020, 47(1): 190304. doi: 10.12086/oee.2020.190304

Citation:

Li P, Liang D, Liang D T, et al. Research on defect inspection method of pipeline robot based on adaptive image enhancement[J]. Opto-Electron Eng, 2020, 47(1): 190304. doi: 10.12086/oee.2020.190304

Research on defect inspection method of pipeline robot based on adaptive image enhancement

Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, Zhejiang 315211, China

Fund Project: Supported by National Natural Science Foundation of China (51805280) and Natural Science Foundation of Zhejiang Province (LQ18E050005)

More Information

^*Corresponding author: Liang Dan, E-mail:liangdan@nbu.edu.cn

Received Date 04 June 2019

Revised Date 04 September 2019

Published Date 01 January 2020

Abstract

Abstract

In view of the problem about uneven image acquisition and inaccurate edge extraction in pipeline detection process, a pipeline robot defect inspection method based on adaptive image enhancement is proposed. Firstly, a single-scale Retinex adaptive image enhancement algorithm is designed, which uses the guided filter to estimate the illumination component of the Value component of the image, and gets the illumination equilibrium image by adaptive Gamma correction, so as to realize the image enhancement. Then, the traditional Canny edge detection method is improved, using bilateral filtering to smooth the image. Besides, the defect images are segmented by the iterative threshold method, and the edge connection is carried out according to the edge pixel similarity. Therefore, the defect contour of the pipe-wall is extracted effectively. Thirdly, a pipeline robot defect detection system based on adaptive image enhancement is built, and a crawler car equipped with the pan-tilt-zoom camera conducts all-round visual inspection of the defects in the pipeline inner wall. The experimental results show that the detection method in this paper can adaptively correct the image brightness, and the uneven brightness of the image is significantly improved. Compared with the sub-optimal algorithm, the information entropy of the image is increased by 2.4%, the average gradient of the image is increased by 2.3%, and the peak signal to noise ratio is increased by 4.4%, and the pipeline defect edges are extracted effectively with the detection accuracy up to 97%.
- pipeline robot /
- adaptive image enhancement /
- Gamma correction /
- defect inspection

FullText(HTML)

References

[1]	洪鼎华, 王环丽, 李武平, 等.在役P92钢蒸汽管道焊接接头中缺陷的研究[J].机械工程学报, 2017, 53(18): 113–120. doi: 10.3901/JME.2017.18.113 CrossRef Google Scholar Hong D H, Wang H L, Li W P, et al. Study on the defects in welded joint of hot reheat steam pipe of ultra supercritical units after long-term service[J]. Journal of Mechanical Engineering, 2017, 53(18): 113–120. doi: 10.3901/JME.2017.18.113 CrossRef Google Scholar
[2]	杨先凤, 吴媛媛, 赵玲.基于Canny改进算子的油管裂纹检测算法[J].计算机工程与设计, 2018, 39(3): 798–803. doi: 10.16208/j.issn1000-7024.2018.03.035 CrossRef Google Scholar Yang X F, Wu Y Y, Zhao L. Pipeline crack detection algorithm based on Canny detector[J]. Computer Engineering and Design, 2018, 39(3): 798–803. doi: 10.16208/j.issn1000-7024.2018.03.035 CrossRef Google Scholar
[3]	孙丽, 孙茜茜, 任亮, 等.应用光纤布喇格光栅传感器监测地下管道腐蚀的新方法研究[J].光子学报, 2012, 41(1): 6–10. doi: 10.3788/gzxb20124101.0006 CrossRef Google Scholar Sun L, Sun Q Q, Ren L, et al. A new method for underground pipeline corrosion monitoring applied FBG[J]. Acta Photonica Sinica, 2012, 41(1): 6–10. doi: 10.3788/gzxb20124101.0006 CrossRef Google Scholar
[4]	曹建树, 曹振, 赵龙飞, 等.激光超声管道表面裂纹检测技术[J].光电工程, 2016, 43(3): 1–6. doi: 10.3969/j.issn.1003-501X.2016.03.001 CrossRef Google Scholar Cao J S, Cao Z, Zhao L F, et al. Detecting techniques of surface crack of pipeline based on laser ultrasonic[J]. Opto-Electronic Engineering, 2016, 43(3): 1–6. doi: 10.3969/j.issn.1003-501X.2016.03.001 CrossRef Google Scholar
[5]	张盼, 陈志东, 李晓旭, 等.基于小波变换的X射线数字图像焊缝缺陷边缘检测[J].管道技术与设备, 2016(3): 41–43. doi: 10.3969/j.issn.1004-9614.2016.03.013 CrossRef Google Scholar Zhang P, Chen Z D, Li X X, et al. X-ray digital image of weld defect based on wavelet transform edge detection[J]. Pipeline Technique and Equipment, 2016(3): 41–43. doi: 10.3969/j.issn.1004-9614.2016.03.013 CrossRef Google Scholar
[6]	王志刚, 罗清旺, 师奕兵, 等.铁磁性管道内涡流线圈耦合分析与管道参数检测[J].仪器仪表学报, 2014, 35(12): 2843–2851. doi: 10.3969/j.issn.0254-3087.2014.12.024 CrossRef Google Scholar Wang Z G, Luo Q W, Shi Y B, et al. Analysis of eddy current coil coupling in ferromagnetic pipe and pipe's parameter detection[J]. Chinese Journal of Scientific Instrument, 2014, 35(12): 2843–2851. doi: 10.3969/j.issn.0254-3087.2014.12.024 CrossRef Google Scholar
[7]	杨金旭, 李策, 夏胜, 等.基于磁致伸缩导波的管道缺陷检测系统[J].仪表技术与传感器, 2017(6): 95–97, 167. doi: 10.3969/j.issn.1002-1841.2017.06.023 CrossRef Google Scholar Yang J X, Li C, Xia S, et al. Magnetostrictive guided wave flaw detection system for pipeline[J]. Instrument Technique and Sensor, 2017(6): 95–97, 167. doi: 10.3969/j.issn.1002-1841.2017.06.023 CrossRef Google Scholar
[8]	Su T C, Yang M D. Application of morphological segmentation to leaking defect detection in sewer pipelines[J]. Sensors, 2014, 14(5): 8686–8704. doi: 10.3390/s140508686 CrossRef Google Scholar
[9]	Vriesman D, Britto A S, Zimmer A, et al. Automatic visual inspection of thermoelectric metal pipes[J]. Signal, Image and Video Processing, 2019, 13(5): 975–983. doi: 10.1007/s11760-019-01435-2 CrossRef Google Scholar
[10]	Yang Z Y, Lu S H, Wu T, et al. Detection of morphology defects in pipeline based on 3D active stereo omnidirectional vision sensor[J]. IET Image Processing, 2018, 12(4): 588–595. doi: 10.1049/iet-ipr.2017.0616 CrossRef Google Scholar
[11]	王颖, 张瑞.管道内表面圆结构光视觉三维测量系统[J].红外与激光工程, 2014, 43(3): 891–896. doi: 10.3969/j.issn.1007-2276.2014.03.040 CrossRef Google Scholar Wang Y, Zhang R. In-pipe surface circular structured light 3D vision inspection system[J]. Infrared and Laser Engineering, 2014, 43(3): 891–896. doi: 10.3969/j.issn.1007-2276.2014.03.040 CrossRef Google Scholar
[12]	吴斌, 邵震宇, 张云昊.微细管道内壁缺陷测量系统构建和技术[J].光电子·激光, 2014, 25(2): 293–298. Google Scholar Wu B, Shao Z Y, Zhang Y H. A new technology of building up defect measuring system for inner micro-pipe[J]. Journal of Optoelectronics·Laser, 2014, 25(2): 293–298. Google Scholar
[13]	Haertel M E M, da Costa Pinto T L F, Júnior A A G. Trinocular stereo system with object space oriented correlation for inner pipe inspection[J]. Measurement, 2015, 73: 162–170. doi: 10.1016/j.measurement.2015.05.015 CrossRef Google Scholar
[14]	Land E H, McCann J J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971, 61(1): 1–11. doi: 10.1364/JOSA.61.000001 CrossRef Google Scholar
[15]	Bradley D, Roth G. Adaptive thresholding using the integral image[J]. Journal of Graphics Tools, 2007, 12(2): 13–21. doi: 10.1080/2151237X.2007.10129236 CrossRef Google Scholar
[16]	魏伟一, 任小康.基于图像信息熵的四叉树检索算法[J].佳木斯大学学报(自然科学版), 2005, 23(4): 511–514, 521. doi: 10.3969/j.issn.1008-1402.2005.04.002 CrossRef Google Scholar Wei W Y, Ren X K. Quadtree image retrieval based on image entropy[J]. Journal of Jiamusi University (Natural Science Edition), 2005, 23(4): 511–514, 521. doi: 10.3969/j.issn.1008-1402.2005.04.002 CrossRef Google Scholar
[17]	任洪娥, 刘冕, 董本志.基于改进形态学算子的木粉边缘检测算法[J].计算机工程与应用, 2015, 51(5): 183–186. doi: 10.3778/j.issn.1002-8331.1304-0316 CrossRef Google Scholar Ren H E, Liu M, Dong B Z. Edge detection algorithm of wood-flour based on modified morphological operator[J]. Computer Engineering and Applications, 2015, 51(5): 183–186. doi: 10.3778/j.issn.1002-8331.1304-0316 CrossRef Google Scholar
[18]	郝利华, 王明泉.改进的Canny算法在直焊缝图像缺陷边缘检测中的应用[J].火力与指挥控制, 2017, 42(7): 52–55. doi: 10.3969/j.issn.1002-0640.2017.07.012 CrossRef Google Scholar Hao L H, Wang M Q. Application of improved canny algorithm in defect edge detection of straight weld image[J]. Fire Control & Command Control, 2017, 42(7): 52–55. doi: 10.3969/j.issn.1002-0640.2017.07.012 CrossRef Google Scholar
[19]	林卉, 赵长胜, 舒宁.基于Canny算子的边缘检测及评价[J].黑龙江工程学院学报, 2003, 17(2): 3–6, 16. doi: 10.3969/j.issn.1671-4679.2003.02.001 CrossRef Google Scholar Lin H, Zhao C S, Shu N. Edge detection based on Canny operator and evaluation[J]. Journal of Heilongjiang Institute of Technology, 2003, 17(2): 3–6, 16. doi: 10.3969/j.issn.1671-4679.2003.02.001 CrossRef Google Scholar
[20]	陈宏希.基于品质因数的边缘检测算子性能优劣客观评价研究[J].自动化与仪器仪表, 2015(8): 8–10. doi: 10.14016/j.cnki.1001-9227.2015.08.008 CrossRef Google Scholar Chen H X. Study on the objective evaluation of the performance of edge detection operator based on the quality factor[J]. Automation & Instrumentation, 2015(8): 8–10. doi: 10.14016/j.cnki.1001-9227.2015.08.008 CrossRef Google Scholar

Overview

Overview

Overview: Digital image processing technology is widely used in the regular detection and maintenance of damaged, aged, faulted pipeline, on account of the virtue of high efficiency, accurate identification, non-contact detection, etc. Aiming at the problem of uneven image acquisition and inaccurate edge extraction in closed pipeline detection process, a pipeline robot defect detection system based on adaptive image enhancement is designed with the pan-tilt-zoom camera as the image acquisition module, Raspberry PI as the image processing system and Arduino as the driving control module to carry on the omni-directional visual inspection to the pipeline inner wall.

A single-scale Retinex adaptive image enhancement algorithm based on guided filtering is proposed. According to the single-scale Retinex theory, the low frequency irradiation component and the high frequency reflection component can be effectively separated from the Value component of HSV space (converted form RGB images) by using the guided filter. The local filter is used to reduce the noise of the reflection component which is mostly distributed in the high frequency part, and the irradiation component is corrected by the adaptive Gamma algorithm. Finally, the integrated restoration of the corrected RGB image of pipeline defect is realized, and the adaptive image enhancement is achieved.

In order to solve the problem of edge blur and threshold setting in traditional Canny detection, bilateral filtering is used to smooth the image and maintain the image edge information effectively. The gradient amplitude is calculated in multiple directions for non-maximum suppression, the adaptive optimal threshold is obtained by iterative threshold method, and the threshold segmentation of the image is carried out. Finally, the edge connection is carried out according to the similarity of edge pixels to realize the accurate extraction of pipeline defect edges.

The experimental results show that the detection system can adapt to correct the image brightness, the uneven illumination of the acquired images is improved obviously. Compared with the suboptimal algorithm, the information entropy of the defect image increases by 2.4%, the average gradient increases by 2.3%, the peak signal to noise ratio increases by 4.4%, and the improved Canny detection algorithm can extract the edge of pipeline defects effectively with the detection accuracy up to 97%. In this paper, the defect detection system of pipeline robot based on adaptive image enhancement can be used to detect and identify pipeline defects in closed pipeline under uneven illumination environment with high detection accuracy, compact structure and strong applicability.