Vision-Based Defect Inspection for Sewer Pipes

Vision-Based Defect Inspection for Sewer Pipes: Comparison

Please note this is a comparison between Version 3 by Peter Tang and Version 2 by Peter Tang.

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

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

	ID	Number of Images	Algorithm	Camera Feature	Country		Strong Point
	ID	Number of Images	Algorithm	Task		Performance			Ref.
	Accuracy (%)	Processing Speed		Task					Ref.
	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

● 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 preseaperrch 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			Precision		Broken, crack, deposit, fracture, hole, root, tap			The proportion of positive samples in all positive prediction samples		NA			^[		NA		⁹		4056		^]		Canada
	1			3 classes			Multiple binary CNNs			Classification			Accuracy: 86.2		Precision: 87.7		Recall: 90.6			NA		^[9]
		USA
	2			Connection, crack, debris, deposit, infiltration, material change, normal, root			1440 × 720–320 × 256			RedZone^®		Solo CCTV crawler		● Slim tractor profile			12,000		● Superior lateral camera		● Simultaneously acquire mainline and lateral videos
^[	³⁶	^]			Recall
	2		The proportion of positive prediction samples in all positive samples					12 classes		^[36]
	Single CNN			Classification		USA			AUROC: 87.1		AUPR: 6.8			NA		^[36]			^[36]		wolverine® 2.02
		3		ARIES INDUSTRIES			Attached deposit, defective connection, displaced joint, fissure, infiltration, ingress, intruding connection, porous, root, sealing, settled deposit, surface		150–450 mm			Accuracy
	3			The proportion of correct prediction in all prediction samples		1040 × 1040		NA					Dataset 1: 2 classes		^[USA		³⁶2,202,582		● Powerful crawler to maneuver obstacles		● Minimum set uptime			The Netherlands ● Camera with lens cleaning technique
	3			Front-facing and back-facing camera with a 185∘ wide lens				^]		^[37]				X5-HS (https://goolnk.com/Rym02W accessed on 20 February 2022)
	Two-level hierarchical CNNs			Classification			Accuracy: 94.5		Precision: 96.84		Recall: 92		F1-score: 94.36			1.109 h for 200 videos			^[38]			4			EASY-SIGHT			300–3000 mm			F1-score		Dataset 1: defective, normal		≥2 million pixels			Harmonic mean of precision and recall		NA	^[38]	NA	China	40,000	● High-definition	● Freely choose wireless and wired connection and control
	Two-level hierarchical CNNs			Classification			Dataset 2: 6 classes			● Display and save videos in real time						1.109 h for 200 videos			^[38]			4																		NA		NA
		Accuracy: 94.96		Precision: 85.13		Recall: 84.61		F1-score: 84.86		China				^[38]			Robocam 6 (https://goolnk.com/43pdGA accessed on 20 February 2022)			TAP Electronics			600 mm or more
	Dataset 2: barrier, deposit, disjunction, fracture, stagger, water		Sony 130-megapixel Exmor 1/3-inch CMOS			Korea				China		● High-resolution		^[38]			15,000		● All-in-one subtitle system
		FAR
	4		False alarm rate in all prediction samples			8 classes		^[55]			Deep CNN			Classification			Accuracy: 64.8			NA				RoboCam Innovation4			TAP Electronics
^[	³⁹		5
Deposits, normal, root

^]		True accuracy			The proportion of all predictions excluding the missed defective images among the entire actual images			^[65]
	Broken, deformation, deposit, other, joint offset, normal, obstacle, water		600 mm or more				1435 × 1054–296 × 166		Sony 130-megapixel Exmor 1/3-inch CMOS			NA		Korea			● Best digital record performance		● Super white LED lighting		● Cableless
	18,333			China			^[39]			AUROC			Area under the receiver operator characteristic (ROC) curve			^[37]

	5			6 classes			CNN			Classification			Accuracy: 96.58			NA			^[41]			Robocam 30004			TAP Electronics’ Japanese subsidiary			250–3000 mm			Sony 1.3-megapixel Exmor CMOS color
	6			Attached deposits, collapse, deformation, displaced joint, infiltration, joint damage, settled deposit			NA			NA
	6			Japan			8 classes			CNN		1045		● Can be utilized in huge pipelines		China			^[		Classification		⁴⁰		Accuracy: 97.6		^]
	0.15 s/image			^[	⁴²	^]			7		AUPR		Circumferential crack, longitudinal crack, multiple crack
	7		Area under the precision-recall curve		320 × 240				7 classes		^[37]		NA			335			USA			^[11]

	Multi-class random forest			Classification			Accuracy: 71			25 FPS			^[43]		8			mAP		Debris, joint faulty, joint open, longitudinal, protruding, surface
	8		NA			mAP first calculates the average precision values for different recall values for one class, and then takes the average of all classes		Robo Cam 6 with a 1/3-in. SONY Exmor CMOS camera			7 classes		^[9]			48,274			South Korea			^[41]

	SVM			Classification			Accuracy: 84.1			NA			^[40]		9			Detection rate		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]
	The ratio of the number of the detected defects to total number of defects
	9			3 classes		^[57]
	SVM			Classification			Recall: 90.3		Precision: 90.3			10 FPS			^[11]		10				Error rate		Crack, deposit, else, infiltration, joint, root, surface			The ratio of the number of mistakenly detected defects to the number of non-defects		NA			^[57]		Remote cameras			2424		UK
	10			^[		3 classes^43]
		CNN			Classification			Accuracy: 96.7		Precision: 99.8		Recall: 93.6		F1-score: 96.6			15 min 30 images			^[51]			11			Broken, crack, deposit, fracture, hole, root, tap				PA		NA			Pixel accuracy calculating the overall accuracy of all pixels in the image
	11			3 classes		^[48]		NA
	RotBoost and statistical feature vector			Classification		1451			Canada			^[		Accuracy: 89.96		^44]
	1.5 s/image			^[	⁵²	^]			12			mPA		Crack, deposit, infiltration, root			1440 × 720–320 × 256			RedZone^®
	12		Solo CCTV crawler		The average of pixel accuracy for all categories				7 classes		^[48]		3000			USA			^[45]
	Neuro-fuzzy classifier			Classification			Accuracy: 91.36			NA			^[53]			13			mIoU			The ratio of intersection and union between predictions and GTs
	13		Connection, fracture, root				4 classes		1507 × 720–720 × 576			^[48]		Front facing CCTV cameras
	Multi-layer perceptions			Classification		3600				Accuracy: 98.2		USA				NA		^[46]
	^[	⁵⁴	^]			14			fwIoU		Crack, deposit, root
	14		Frequency-weighted IoU measuring the mean IoU value weighing the pixel frequency of each class			2 classes		928 × 576–352 × 256			^[48		NA		^]		3000			USA			Rule-based classifier			Classification			Accuracy: 87		^[47]
FAR: 18		Recall: 89			NA			^[55]			15			Crack, deposit, root			512 × 256			NA			1880			USA
	15			2 classes			OCSVM			Classification			Accuracy: 75		^[48]
	NA			^[	⁵⁸	^]			16			Crack, infiltration, joint, protruding			1073 × 749–296 × 237			NA			1106			China			^[49]
	17			Crack, non-crack			64 × 64			NA			40,810			Australia			^[50]
	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			Crack, normal, spalling			4000 × 46,000–3168 × 4752			Canon EOS. Tripods and stabilizers		294		China
	18			4 classes			RBN			Classification			Accuracy: 95		^[51]
	NA			^[	⁶⁴	^]			19			Collapse, crack, root			NA			SSET system			239
	19			7 classes			YOLOv3		USA			Detection			mAP: 85.37			33 FPS		^[52]
	^[	⁹	^]			20			Clean pipe, collapsed pipe, eroded joint, eroded lateral, misaligned joint, perfect joint, perfect lateral			NA			SSET system		Detection		500			USA			^[
	20		⁵³	^]
	4 classes			Faster R-CNN			mAP: 83			9 FPS			^[45]			21			Cracks, joint, reduction, spalling			512 × 512			CCTV or Aqua Zoom camera			1096			Canada			^[54]

	21			3 classes			Faster R-CNN			Detection			mAP: 77			110 ms/image			^[46]			22			Defective, normal			NA			CCTV (Fisheye)
	22			192			3 classes		USA			Faster R-CNN		^[		Detection			Precision: 88.99		Recall: 87.96		F1-score: 88.21			110 ms/image		^55]
	^[	⁴⁷	^]			23				1507 × 720–720 × 576
	23			Front-facing CCTV cameras				2 classes3800				CNN			DetectionUSA				Accuracy: 96		Precision: 90			0.2782 s/image		^[56]
	^[	⁵⁰	^]			24			Crack, non-crack			240 × 320
	24			3 classes				Faster R-CNNCCTV			200			South Korea			^[57]
							Detection			mAP: 71.8			110 ms/image			^[63]			25			Faulty, normal			NA			CCTV			8000
										SSD			UK						^[58]
	mAP: 69.5			57 ms/image						26
	YOLOv3		Blur, deposition, intrusion, obstacle			NA			CCTV			12,000			mAP: 53		NA			^[59]
		33 ms/image			27			Crack, deposit, displaced joint, ovality			NA			CCTV (Fisheye)
	25			2 classes			Rule-based detector			Detection		32			Detection rate: 89.2		Error rate: 4.44		Qatar			1 FPS		^[60]
	^[	⁵⁷	^]			29			Crack, non-crack
	26			2 classes		320 × 240–20 × 20			GA and CNN		CCTV			100			NA			Detection			Detection rate: 92.3		^[		NA		^61]
	^[	⁶¹	^]			30			Barrier, deposition, distortion, fraction, inserted			600 × 480			CCTV and quick-view (QV) cameras			10,000
	27			5 classes		China				SRPN			Detection			mAP: 50.8		Recall: 82.4		^[62]
	153 ms/image			^[	⁶²	^]			31			Fracture			NA			CCTV
	28			1 class			CNN and YOLOv32100				Detection			AP: 71USA				65 ms/image		^[63]
	^[	⁶⁶	^]			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.

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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