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Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

Department of Civil Engineering and Engineering Management, National Quemoy University, Da Xue Rd. 1, Kinmen 892, Taiwan
Department of Civil Engineering, National Chung Hsing University, Taichung 402, Taiwan
Author to whom correspondence should be addressed.
Sensors 2014, 14(5), 8686-8704;
Received: 13 March 2014 / Revised: 18 April 2014 / Accepted: 12 May 2014 / Published: 16 May 2014
(This article belongs to the Special Issue Sensors for Fluid Leak Detection)
PDF [764 KB, uploaded 21 June 2014]


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 identified 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. View Full-Text
Keywords: leaking; sewer pipeline; computer vision; defect detection; morphology leaking; sewer pipeline; computer vision; defect detection; morphology
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Su, T.-C.; Yang, M.-D. Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines. Sensors 2014, 14, 8686-8704.

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