A Survey on Image-Based Automation of CCTV and SSET Sewer Inspections

Haurum, Joakim Bruslund; Moeslund, Thomas B.

doi:10.1016/j.autcon.2019.103061

Cited by 85 publications

(43 citation statements)

References 74 publications

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“…. Table 4 shows that year of construction is the most important feature in 13 of the utilities followed by age (8), groundwater (6), year of rehabilitation (5), and dimension (1). In general, there is a tendency for the continuous variables to be found relevant more often than categorical and binary variables.…”

Section: Difference Between Utilitiesmentioning

confidence: 99%

“…Lack of publicly available data [5], numerous different standards for CCTV-inspections and different methods for evaluation of sewer deterioration models complicate the comparison of deterioration models. This also applies to the performance obtained in experiment two, where the f1-score drops from 0.73 to 0.35 when solely considering pipes in CS four as being in bad condition instead of considering pipes in both CS three and four.…”

Section: Definition Of Target Variablementioning

confidence: 99%

“…CCTV inspection is done by manually sending a TV-inspection robot into the sewer and annotating all observations. As this is very time consuming, expensive, and imprecise due to a number of subjective factors [3,4], much research has been put into automating these processes [1,5]. However, full automatization of sewer inspection is not imminent.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of deterioration models have been developed; however, a lack of publicly available datasets due to privacy issues makes it difficult to compare the models [5]. Furthermore, the condition state (CS) is typically based on the local standard for CCTV inspection, which can be based on, for example, the European standard [13], Pipeline Assessment Certification Program [14], or a country specific standard [12,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Feature Analysis for Sewer Deterioration Modeling

Hansen

Rasmussen²,

Uggerby³

et al. 2021

Water

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Timely maintenance of sewers is essential to preventing reduced functionality and breakdown of the systems. Due to the high costs associated with inspecting a sewer system, substantial research has focused on sewer deterioration modeling and identification of the most useful features. However, there is a lack of consensus in the findings. This study investigates how the feature importance depends on the definition of bad pipes and how the feature importance changes between utilities with similar data bases. A dataset containing 318,457 pipes from 35 utilities with a condition state (CS) ranging from one to four was used. The dataset was cleaned, and a backward step analysis (BSA) was applied to two ways of binarizing the CS. Additionally, a BSA was applied for each utility with ≥100 pipes in CS four. The results showed that a selective definition of bad pipes reduced the performance and changed the order of which features contributed the most. In each case, either year of construction, age, groundwater, year of rehabilitation, or dimension was the most important feature. On average 6.5 features contributed to the utility-specific models. The feature analysis was sensitive to the inspection strategy, the size of the dataset, and interdependency between the features.

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Section: Difference Between Utilitiesmentioning

confidence: 99%

Section: Definition Of Target Variablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comprehensive Feature Analysis for Sewer Deterioration Modeling

Hansen

Rasmussen²,

Uggerby³

et al. 2021

Water

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“…Layers of features, which are not designed by engineers of a specific field, are used for general-purpose learning. Some studies have automatically detected defects from CCTV footage using these advantages [12][13][14], but they were limited to functional defects inside pipes such as cracks, deposits, and tree roots. These studies used software applications to investigate the types of defects that can be identified using existing CCTVs.…”

Section: Introductionmentioning

confidence: 99%

Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning

Yoo

Koo

et al. 2021

Water

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The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation.

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