Robust adaptive flow line detection in sewer pipes

Kirstein, Simon; Müller, Karsten; Walecki-Mingers, Mark; Deserno, Thomas M.

doi:10.1016/j.autcon.2011.05.009

Cited by 23 publications

(13 citation statements)

References 17 publications

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“…Moreover, some morphology-based decision criteria or fitness function [ 37 ] could be established in the future for removal of the noisy environment to improve the correctness and quality of detection. According to the comment by Kirstein et al [ 29 ], the irregular environment inside sewer pipelines results in a long time still being needed for developing an automated sewer inspection system. In further study, ground light detection and ranging (LiDAR) can be introduced and coupled with a synchronous camera for pipeline defect measurement based on point clouds with precise coordinates.…”

Section: Discussionmentioning

confidence: 99%

“…To effectively and correctly detect sewer pipeline defects, this paper presents a novel algorithm, morphological segmentation based on edge detection (MSED), to segment sewer pipeline defects from CCTV inspection images. Edge detection is considered as an important pre-processing step in image segmentation [ 27 – 29 ]. Based on edge detection, MSED attempts to search complete and correct image regions of sewer pipeline defects in CCTV images.…”

Section: Introductionmentioning

confidence: 99%

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Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

Yang

2014

Sensors

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As one of major underground pipelines, sewerage is an important infrastructure in any modern city. The most common problem occurring in sewerage is leaking, whose position and failure level is typically idengified through closed circuit television (CCTV) inspection in order to facilitate rehabilitation process. This paper proposes a novel method of computer vision, morphological segmentation based on edge detection (MSED), to assist inspectors in detecting pipeline defects in CCTV inspection images. In addition to MSED, other mathematical morphology-based image segmentation methods, including opening top-hat operation (OTHO) and closing bottom-hat operation (CBHO), were also applied to the defect detection in vitrified clay sewer pipelines. The CCTV inspection images of the sewer system in the 9th district, Taichung City, Taiwan were selected as the experimental materials. The segmentation results demonstrate that MSED and OTHO are useful for the detection of cracks and open joints, respectively, which are the typical leakage defects found in sewer pipelines.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

Yang

2014

Sensors

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“…The advanced technologies for the inspection of sewer systems include sonarbased and laser-based scanning, digital side scanners, and ground penetrating radar [12][13][14][15]. However, there are some limitations for these technologies.…”

Section: Located In the Bangkok Metropolitan Area Dindaeng Water Environmentmentioning

confidence: 99%

Inspection and Understanding of Sewer Network Condition in Dindaeng District, Bangkok, Thailand

Phyu

Khaodhiar

2021

App. Envi. Res.

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Sewer pipelines are usually operated with little maintenance after installation. After years in service, sewer pipes get old and deteriorated. The present research is the first study that attempts to inspect the condition of sewer pipelines in the Bangkok Metropolitan Area. A robot with Closed Circuit Television (CCTV) was used to capture video and images of the interior of the sewer pipelines between two manholes. The system was operated by Utility Business Alliance (UBA) Company. The condition of the sewer pipelines was visually analyzed in line with the scoring and grading system of New Zealand’s pipe inspection manual. CCTV footage was received from UBA, and each meter of the sewer pipelines was visually inspected, scored, and graded. The study area included 22 randomly selected roads around Dindaeng District. For the structural condition, surface damage was found in all of the 89 inspected pipes. Joint faulty and crack longitudinal were found in approximately 60% of the inspected sewer pipelines. Infiltration present was found in 33% of the inspected pipes, while crack circumferential and crack multiple were minimal in the study area. For the service condition, obstruction temporary, debris silty, and debris greasy were found in 15%, 11%, and 4% of the inspected sewer pipelines, respectively. The grades of the structural condition were mainly between 4 and 5, indicating that most of the sewer pipelines in the area would need rehabilitation programs. The grades of the service condition were between excellent and good. This is the first time that such a program has been conducted in Thailand. The information of the condition of the pipelines and the inspection and characterization methods should be used as a reference for maintenance programs and future assessment programs. The adjustment or development of the inspection manual should be done in collaboration with local contacts.

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“…It should be noted that Wu et al [114] actually use the front camera of the SSET device unlike the other mentioned papers, which use the unwrapped pipe wall images. In a few cases, CCTV footage has been recorded with a fish-eye lens that has either been unwrapped in order to obtain an image of the entire pipe wall, similar to SSET [115][116][117][118][119][120][121][122][123], or simply used the original wide-angle images [45,[124][125][126][127][128][129][130][131][132][133][134][135]. A zoom CCTV camera has also been utilized [136].…”

Section: Acquisitionmentioning

confidence: 99%

“…Several systems convert the RGB images to grayscale by averaging the color channels [33, 58, 59, 63, 64, 67-74, 92, 94, 95, 117, 124]. Weighted averages such as the NTSC standard [135], brightness [126,127], luminance [62,114], based on the pipe material [116], or a linear transformation based on Fischer's linear discriminant classifier [96,100,102,105,109,110] have also been utilized when emphasis on specific color combinations was necessary. Color conversion has also been used in conjunction with image enhancement [111][112][113].…”

Section: Color Spacementioning

confidence: 99%

A Deep Dive into Computer Vision Aided Sewer Inspections

Haurum

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Robust adaptive flow line detection in sewer pipes

Cited by 23 publications

References 17 publications

Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

Inspection and Understanding of Sewer Network Condition in Dindaeng District, Bangkok, Thailand

A Deep Dive into Computer Vision Aided Sewer Inspections

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