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Wang, H.; Li, Y.; Dang, L.M.; Hyoung-Kyu, S.; Moon, H. Vision-Based Defect Inspection for Sewer Pipes. Encyclopedia. Available online: https://encyclopedia.pub/entry/22267 (accessed on 19 June 2024).
Wang H, Li Y, Dang LM, Hyoung-Kyu S, Moon H. Vision-Based Defect Inspection for Sewer Pipes. Encyclopedia. Available at: https://encyclopedia.pub/entry/22267. Accessed June 19, 2024.
Wang, Hanxiang, Yanfen Li, L. Minh Dang, Song Hyoung-Kyu, Hyeonjoon Moon. "Vision-Based Defect Inspection for Sewer Pipes" Encyclopedia, https://encyclopedia.pub/entry/22267 (accessed June 19, 2024).
Wang, H., Li, Y., Dang, L.M., Hyoung-Kyu, S., & Moon, H. (2022, April 26). Vision-Based Defect Inspection for Sewer Pipes. In Encyclopedia. https://encyclopedia.pub/entry/22267
Wang, Hanxiang, et al. "Vision-Based Defect Inspection for Sewer Pipes." Encyclopedia. Web. 26 April, 2022.
Vision-Based Defect Inspection for Sewer Pipes
Edit

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. Since it was first introduced in the 1960s, computer vision (CV) has become a mature technology that is used to realize promising automation for sewer inspections.

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 available techniques such as closed-circuit television (CCTV), sewer scanner and evaluation technology (SSET), and totally integrated sonar and camera systems (TISCITs) [5]. CCTV-based inspections rely on a remotely controlled tractor or robot with a mounted CCTV camera [6]. An SSET is a type of method that acquires the scanned data from a suite of sensor devices [7]. The TISCIT system utilizes sonar and CCTV cameras to obtain a 360° view of the sewer conditions [5]. As mentioned in several studies [6][8][9][10], CCTV-based inspections are the most widely used methods due to the advantages of economics, safety, and simplicity. Nevertheless, the performance of CCTV-based inspections is limited by the quality of the acquired data. Therefore, image-based learning methods require pre-processing algorithms to remove noise and enhance the resolution of the collected images. Many studies on sewer inspections have recently applied image pre-processing before examining the defects [11][12][13].
Figure 1. There are five stages in the defect inspection framework, which include (a) the data acquisition stage based on various sensors (CCTV, sonar, or scanner), (b) the data processing stage for the collected data, (c) the defect inspection stage containing different algorithms (defect classification, detection, and segmentation), (d) the risk assessment for detected defects using image post-processing, and (e) the final report generation stage for the condition evaluation.

2. Defect Inspection

In this section, several classic algorithms are illustrated, and the research tendency is analyzed. Figure 2 provides a brief description of the algorithms in each category.  In order to comprehensively analyze these studies, the publication time, title, utilized methodology, advantages, and disadvantages for each study are covered. Moreover, the specific proportion of each inspection algorithm is computed in Figure 3. It is clear that the defect classification accounts for the most significant percentages in all the investigated studies.
Figure 2. The classification map of the existing algorithms for each category. The dotted boxes represent the main stages of the algorithms.
Figure 3. Proportions of the investigated studies using different inspection algorithms.

2.1. Defect Classification

Due to the recent advancements in ML, both the scientific community and industry have attempted to apply ML-based pattern recognition in various areas, such as agriculture [14], resource management [15], and construction [16]. At present, many types of defect classification algorithms have been presented for both binary and multi-class classification tasks.

2.2. Defect Detection

Rather than the classification algorithms that merely offer each defect a class type, object detection is conducted to locate and classify the objects among the predefined classes using rectangular bounding boxes (BBs) as well as confidence scores (CSs). In recent studies, object detection technology has been increasingly applied in several fields, such as intelligent transportation [17][18][19], smart agriculture [20][21][22], and autonomous construction [23][24][25]. The generic object detection consists of the one-stage approaches and the two-stage approaches. The classic one-stage detectors based on regression include YOLO [26], SSD [27], CornerNet [28], and RetinaNet [29]. The two-stage detectors are based on region proposals, including Fast R-CNN [30], Faster R-CNN [31], and R-FCN [32].

2.3. Defect Segmentation

Defect segmentation algorithms can predict defect categories and pixel-level location information with exact shapes, which is becoming increasingly significant for the research on sewer condition assessment by re-coding the exact defect attributes and analyzing the specific severity of each defect. The previous segmentation methods were mainly based on mathematical morphology [33][34]. However, the morphology segmentation approaches were inefficient compared to the DL-based segmentation methods. As a result, the defect segmentation methods based on DL have been recently explored in various fields.

3. Dataset and Evaluation Metric

The performances of all the algorithms were tested and are reported based on a specific dataset using specific metrics. As a result, datasets and protocols were two primary determining factors in the algorithm evaluation process. The evaluation results are not convincing if the dataset is not representative, or the used metric is poor. It is challenging to judge what method is the SOTA because the existing methods in sewer inspections utilize different datasets and protocols. Therefore, benchmark datasets and standard evaluation protocols are necessary to be provided for future studies.

3.1. Dataset

3.1.1. Dataset Collection

Currently, many data collection robotic systems have emerged that are capable of assisting workers with sewer inspection and spot repair. Table 1 lists the latest advanced robots along with their respective information, including the robot’s name, company, pipe diameter, camera feature, country, and main strong points. Figure 4 introduces several representative robots that are widely utilized to acquire images or videos from underground infrastructures. As shown in Figure 4a, LETS 6.0 is a versatile and powerful inspection system that can be quickly set up to operate in 150 mm or larger pipes. A representative work (Robocam 6) of the Korean company TAP Electronics is shown in Figure 4b. Robocam 6 is the best model to increase the inspection performance without the considerable cost of replacing the equipment. Figure 4c is the X5-HS robot that was developed in China, which is a typical robotic crawler with a high-definition camera. In Figure 4d, Robocam 3000, sold by Japan, is the only large-scale system that is specially devised for inspecting pipes ranging from 250 mm to 3000 mm. It used to be unrealistic to apply the crawler in huge pipelines in Korea.
Figure 4. Representative inspection robots for data acquisition. (a) LETS 6.0, (b) Robocam 6, (c) X5-HS, and (d) Robocam 3000.
Table 1. The detailed information of the latest robots for sewer inspection.

Name

Company

Pipe Diameter

Camera Feature

Country

Strong Point

CAM160 (https://goolnk.com/YrYQob accessed on 20 February 2022)

Sewer Robotics

200–500 mm

NA

USA

● Auto horizon adjustment

● Intensity adjustable LED lighting

● Multifunctional

LETS 6.0 (https://ariesindustries.com/products/ accessed on 20 February 2022)

ARIES INDUSTRIES

150 mm or larger

Self-leveling lateral camera or a Pan and tilt camera

USA

● Slim tractor profile

● Superior lateral camera

● Simultaneously acquire mainline and lateral videos

wolverine® 2.02

ARIES INDUSTRIES

150–450 mm

NA

USA

● Powerful crawler to maneuver obstacles

● Minimum set uptime

● Camera with lens cleaning technique

X5-HS (https://goolnk.com/Rym02W accessed on 20 February 2022)

EASY-SIGHT

300–3000 mm

≥2 million pixels

China

● High-definition

● Freely choose wireless and wired connection and control

● Display and save videos in real time

Robocam 6 (https://goolnk.com/43pdGA accessed on 20 February 2022)

TAP Electronics

600 mm or more

Sony 130-megapixel Exmor 1/3-inch CMOS

Korea

● High-resolution

● All-in-one subtitle system

RoboCam Innovation4

TAP Electronics

600 mm or more

Sony 130-megapixel Exmor 1/3-inch CMOS

Korea

● Best digital record performance

● Super white LED lighting

● Cableless

Robocam 30004

TAP Electronics’ Japanese subsidiary

250–3000 mm

Sony 1.3-megapixel Exmor CMOS color

Japan

● Can be utilized in huge pipelines

● Optical 10X zoom performance

3.1.2. Benchmarked Dataset

Open-source sewer defect data is necessary for academia to promote fair comparisons in automatic multi-defect classification tasks. In this survey, a publicly available benchmark dataset called Sewer-ML [35] for vision-based defect classification is introduced. The Sewer-ML dataset, acquired from Danish companies, contains 1.3 million images labeled by sewer experts with rich experience. Figure 5 shows some sample images from the Sewer-ML dataset, and each image includes one or more classes of defects. The recorded text in the image was redacted using a Gaussian blur kernel to protect private information. Besides, the detailed information of the datasets used in recent papers is described in Table 2. This research summarizes 32 datasets from different countries in the world, of which the USA has 12 datasets, accounting for the largest proportion. The largest dataset contains 2,202,582 images, whereas the smallest dataset has only 32 images. Since the images were acquired by various types of equipment, the collected images have varied resolutions ranging from 64 × 64 to 4000 × 46,000.
Figure 5. Sample images from the Sewer-ML dataset that has a wide diversity of materials and shapes.
Table 2. Research datasets for sewer defects in recent studies.

ID

Defect Type

Image Resolution

Equipment

Number of Images

Country

Ref.

1

Broken, crack, deposit, fracture, hole, root, tap

NA

NA

4056

Canada

[9]

2

Connection, crack, debris, deposit, infiltration, material change, normal, root

1440 × 720–320 × 256

RedZone®

Solo CCTV crawler

12,000

USA

[36]

3

Attached deposit, defective connection, displaced joint, fissure, infiltration, ingress, intruding connection, porous, root, sealing, settled deposit, surface

1040 × 1040

Front-facing and back-facing camera with a 185∘ wide lens

2,202,582

The Netherlands

[37]

4

Dataset 1: defective, normal

NA

NA

40,000

China

[38]

Dataset 2: barrier, deposit, disjunction, fracture, stagger, water

15,000

5

Broken, deformation, deposit, other, joint offset, normal, obstacle, water

1435 × 1054–296 × 166

NA

18,333

China

[39]

6

Attached deposits, collapse, deformation, displaced joint, infiltration, joint damage, settled deposit

NA

NA

1045

China

[40]

7

Circumferential crack, longitudinal crack, multiple crack

320 × 240

NA

335

USA

[11]

8

Debris, joint faulty, joint open, longitudinal, protruding, surface

NA

Robo Cam 6 with a 1/3-in. SONY Exmor CMOS camera

48,274

South Korea

[41]

9

Broken, crack, debris, joint faulty, joint open, normal, protruding, surface

1280 × 720

Robo Cam 6 with a megapixel Exmor CMOS sensor

115,170

South Korea

[42]

10

Crack, deposit, else, infiltration, joint, root, surface

NA

Remote cameras

2424

UK

[43]

11

Broken, crack, deposit, fracture, hole, root, tap

NA

NA

1451

Canada

[44]

12

Crack, deposit, infiltration, root

1440 × 720–320 × 256

RedZone® Solo CCTV crawler

3000

USA

[45]

13

Connection, fracture, root

1507 × 720–720 × 576

Front facing CCTV cameras

3600

USA

[46]

14

Crack, deposit, root

928 × 576–352 × 256

NA

3000

USA

[47]

15

Crack, deposit, root

512 × 256

NA

1880

USA

[48]

16

Crack, infiltration, joint, protruding

1073 × 749–296 × 237

NA

1106

China

[49]

17

Crack, non-crack

64 × 64

NA

40,810

Australia

[50]

18

Crack, normal, spalling

4000 × 46,000–3168 × 4752

Canon EOS. Tripods and stabilizers

294

China

[51]

19

Collapse, crack, root

NA

SSET system

239

USA

[52]

20

Clean pipe, collapsed pipe, eroded joint, eroded lateral, misaligned joint, perfect joint, perfect lateral

NA

SSET system

500

USA

[53]

21

Cracks, joint, reduction, spalling

512 × 512

CCTV or Aqua Zoom camera

1096

Canada

[54]

22

Defective, normal

NA

CCTV (Fisheye)

192

USA

[55]

23

Deposits, normal, root

1507 × 720–720 × 576

Front-facing CCTV cameras

3800

USA

[56]

24

Crack, non-crack

240 × 320

CCTV

200

South Korea

[57]

25

Faulty, normal

NA

CCTV

8000

UK

[58]

26

Blur, deposition, intrusion, obstacle

NA

CCTV

12,000

NA

[59]

27

Crack, deposit, displaced joint, ovality

NA

CCTV (Fisheye)

32

Qatar

[60]

29

Crack, non-crack

320 × 240–20 × 20

CCTV

100

NA

[61]

30

Barrier, deposition, distortion, fraction, inserted

600 × 480

CCTV and quick-view (QV) cameras

10,000

China

[62]

31

Fracture

NA

CCTV

2100

USA

[63]

32

Broken, crack, fracture, joint open

NA

CCTV

291

China

[64]

3.2. Evaluation Metric

The studied performances are ambiguous and unreliable if there is no suitable metric. In order to present a comprehensive evaluation, multitudinous methods are proposed in recent studies. Detailed descriptions of different evaluation metrics are explained in Table 3. Table 4 presents the performances of the investigated algorithms on different datasets in terms of different metrics.
Table 3. Overview of the evaluation metrics in the recent studies.

Metric

Description

Ref.

Precision

The proportion of positive samples in all positive prediction samples

[9]

Recall

The proportion of positive prediction samples in all positive samples

[36]

Accuracy

The proportion of correct prediction in all prediction samples

[36]

F1-score

Harmonic mean of precision and recall

[38]

FAR

False alarm rate in all prediction samples

[55]

True accuracy

The proportion of all predictions excluding the missed defective images among the entire actual images

[65]

AUROC

Area under the receiver operator characteristic (ROC) curve

[37]

AUPR

Area under the precision-recall curve

[37]

mAP

mAP first calculates the average precision values for different recall values for one class, and then takes the average of all classes

[9]

Detection rate

The ratio of the number of the detected defects to total number of defects

[57]

Error rate

The ratio of the number of mistakenly detected defects to the number of non-defects

[57]

PA

Pixel accuracy calculating the overall accuracy of all pixels in the image

[48]

mPA

The average of pixel accuracy for all categories

[48]

mIoU

The ratio of intersection and union between predictions and GTs

[48]

fwIoU

Frequency-weighted IoU measuring the mean IoU value weighing the pixel frequency of each class

[48]

Table 4. Performances of different algorithms in terms of different evaluation metrics.

ID

Number of Images

Algorithm

Task

Performance

Ref.

Accuracy (%)

Processing Speed

1

3 classes

Multiple binary CNNs

Classification

Accuracy: 86.2

Precision: 87.7

Recall: 90.6

NA

[36]

2

12 classes

Single CNN

Classification

AUROC: 87.1

AUPR: 6.8

NA

[36]

3

Dataset 1: 2 classes

Two-level hierarchical CNNs

Classification

Accuracy: 94.5

Precision: 96.84

Recall: 92

F1-score: 94.36

1.109 h for 200 videos

[38]

Dataset 2: 6 classes

Accuracy: 94.96

Precision: 85.13

Recall: 84.61

F1-score: 84.86

4

8 classes

Deep CNN

Classification

Accuracy: 64.8

NA

[39]

5

6 classes

CNN

Classification

Accuracy: 96.58

NA

[41]

6

8 classes

CNN

Classification

Accuracy: 97.6

0.15 s/image

[42]

7

7 classes

Multi-class random forest

Classification

Accuracy: 71

25 FPS

[43]

8

7 classes

SVM

Classification

Accuracy: 84.1

NA

[40]

9

3 classes

SVM

Classification

Recall: 90.3

Precision: 90.3

10 FPS

[11]

10

3 classes

CNN

Classification

Accuracy: 96.7

Precision: 99.8

Recall: 93.6

F1-score: 96.6

15 min 30 images

[51]

11

3 classes

RotBoost and statistical feature vector

Classification

Accuracy: 89.96

1.5 s/image

[52]

12

7 classes

Neuro-fuzzy classifier

Classification

Accuracy: 91.36

NA

[53]

13

4 classes

Multi-layer perceptions

Classification

Accuracy: 98.2

NA

[54]

14

2 classes

Rule-based classifier

Classification

Accuracy: 87

FAR: 18

Recall: 89

NA

[55]

15

2 classes

OCSVM

Classification

Accuracy: 75

NA

[58]

16

4 classes

CNN

Classification

Recall: 88

Precision: 84

Accuracy: 85

NA

[59]

17

2 class

Rule-based classifier

Classification

Accuracy: 84

FAR: 21

True accuracy: 95

NA

[65]

18

4 classes

RBN

Classification

Accuracy: 95

NA

[64]

19

7 classes

YOLOv3

Detection

mAP: 85.37

33 FPS

[9]

20

4 classes

Faster R-CNN

Detection

mAP: 83

9 FPS

[45]

21

3 classes

Faster R-CNN

Detection

mAP: 77

110 ms/image

[46]

22

3 classes

Faster R-CNN

Detection

Precision: 88.99

Recall: 87.96

F1-score: 88.21

110 ms/image

[47]

23

2 classes

CNN

Detection

Accuracy: 96

Precision: 90

0.2782 s/image

[50]

24

3 classes

Faster R-CNN

Detection

mAP: 71.8

110 ms/image

[63]

SSD

mAP: 69.5

57 ms/image

YOLOv3

mAP: 53

33 ms/image

25

2 classes

Rule-based detector

Detection

Detection rate: 89.2

Error rate: 4.44

1 FPS

[57]

26

2 classes

GA and CNN

Detection

Detection rate: 92.3

NA

[61]

27

5 classes

SRPN

Detection

mAP: 50.8

Recall: 82.4

153 ms/image

[62]

28

1 class

CNN and YOLOv3

Detection

AP: 71

65 ms/image

[66]

29

3 classes

DilaSeg-CRF

Segmentation

PA: 98.69

mPA: 91.57

mIoU: 84.85

fwIoU: 97.47

107 ms/image

[48]

30

4 classes

PipeUNet

Segmentation

mIoU: 76.37

32 FPS

[49]

As shown in Table 4, accuracy is the most commonly used metric in the classification tasks [36][38][39][40][41][42][43][51][52][53][54][55][58][59][65]. In addition to this, other subsidiary metrics such as precision [11][36][38][51][59], recall [11][36][38][51][55][59], and F1-score [38][51] are also well supported. Furthermore, AUROC and AUPR are calculated in [37] to measure the classification results, and FAR is used in [55][65] to check the false alarm rate in all the predictions. In contrast to classification, mAP is a principal metric for detection tasks [9][45][46][62][63]. In another study [47], precision, recall, and F1-score are reported in conjunction to provide a comprehensive estimation for defect detection. Heo et al. [57] assessed the model performance based on the detection rate and the error rate. Kumar and Abraham [66] report the average precision (AP), which is similar to the mAP but for each class. For the segmentation tasks, the mIoU is considered as an important metric that is used in many studies [48][49]. Apart from the mIoU, the per-class pixel accuracy (PA), mean pixel accuracy (mPA), and frequency-weighted IoU (fwIoU) are applied to evaluate the segmented results at the pixel level.

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