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

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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

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References

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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.