Citation: Li, Y.; Wang, H.; Dang, L.M.;
Song, H.-K.; Moon, H. Vision-Based
Defect Inspection and Condition
Assessment for Sewer Pipes: A
Comprehensive Survey. Sensors 2022,
22, 2722. https://doi.org/10.3390/
s22072722
Academic Editor: Karim Benzarti
Received: 22 February 2022
Accepted: 30 March 2022
Published: 1 April 2022
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Review
Vision-Based Defect Inspection and Condition Assessment for
Sewer Pipes: A Comprehensive Survey
Yanfen Li
1
, Hanxiang Wang
1
, L. Minh Dang
2
, Hyoung-Kyu Song
2
and Hyeonjoon Moon
1,
*
1
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea;
1826535091@sju.ac.kr (Y.L.); hanxiang@sju.ac.kr (H.W.)
2
Department of Information and Communication Engineering and Convergence Engineering for Intelligent
Drone, Sejong University, Seoul 05006, Korea; minhdl@sejong.ac.kr (L.M.D.); songhk@sejong.ac.kr (H.-K.S.)
* Correspondence: hmoon@sejong.ac.kr
Abstract:
Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques
have recently gained conspicuous advancements, enabling them to be extensively applied for au-
tonomous constructions. Although numerous studies regarding the defect inspection and condition
assessment in underground sewer pipelines have presently emerged, we still lack a thorough and
comprehensive survey of the latest developments. This survey presents a systematical taxonomy
of diverse sewer inspection algorithms, which are sorted into three categories that include defect
classification, defect detection, and defect segmentation. After reviewing the related sewer defect
inspection studies for the past 22 years, the main research trends are organized and discussed in detail
according to the proposed technical taxonomy. In addition, different datasets and the evaluation
metrics used in the cited literature are described and explained. Furthermore, the performances of
the state-of-the-art methods are reported from the aspects of processing accuracy and speed.
Keywords: survey; computer vision; defect inspection; condition assessment; sewer pipes
1. Introduction
1.1. Background
Underground sewerage systems (USSs) are a vital part of public infrastructure that
contributes to collecting wastewater or stormwater from various sources and conveying
it to storage tanks or sewer treatment facilities. A healthy USS with proper functionality
can effectively prevent urban waterlogging and play a positive role in the sustainable
development of water resources. However, sewer defects caused by different influence
factors such as age and material directly affect the degradation of pipeline conditions. It was
reported in previous studies that the conditions of USSs in some places are unsatisfactory
and deteriorate over time. For example, a considerable proportion (20.8%) of Canadian
sewers is graded as poor and very poor. The rehabilitation of these USSs is needed in
the following decade in order to ensure normal operations and services on a continuing
basis [
1
]. Currently, the maintenance and management of USSs have become challenging
problems for municipalities worldwide due to the huge economic costs [
2
]. In 2019, a report
in the United States of America (USA) estimated that utilities spent more than USD 3 billion
on wastewater pipe replacements and repairs, which addressed 4692 miles of pipeline [3].
1.2. Defect Inspection Framework
Since it was first introduced in the 1960s [
4
], computer vision (CV) has become a
mature technology that is used to realize promising automation for sewer inspections.
In order to meet the increasing demands on USSs, a CV-based defect inspection system
is required to identify, locate, or segment the varied defects prior to the rehabilitation
process. As illustrated in Figure 1, an efficient defect inspection framework for underground
sewer pipelines should cover five stages. In the data acquisition stage, there are many
Sensors 2022, 22, 2722. https://doi.org/10.3390/s22072722 https://www.mdpi.com/journal/sensors
