Sewer Pipe Inspection Research Articles

Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

Prediction of failures in sewer networks using various machine learning classifiers

ABSTRACT In sewer networks, failure prediction plays a significant role in operation and maintenance plans of wastewater utilities. This study aims to determine the effective variables on the failures by using feature selection algorithms (FS) and achieve maximum model accuracy with minimum variables. Also, four scenarios based on the suggested FS algorithms were developed. In these scenarios, the best prediction models were investigated using machine learning classifiers (ML) such as neural network classifier (NNC), gradient boosting machine (GBM), random forest (FR), and hybrid model (HM). The classification performance of ML models was evaluated using accuracy, precision, F1_score, and receiver operating characteristics (ROC) curve. The model accuracies ranging from 0.99 for accuracy to 1 for the ROC curve were achieved through ML algorithms. In conclusion, the ML algorithms suggested in this study may be a decision support tool for wastewater utilities in prioritizing the replacement, maintenance, and inspection of sewer pipes.

Carbon footprint assessment of maintenance and rehabilitation techniques for sewer systems

ABSTRACT The research study presented in this paper sets out to improve our understanding of the environmental impacts associated with the maintenance and rehabilitation of conventional gravity sewer systems and, through this, support decision-making processes in sewer system management. The objects assessed are open trench replacement, trenchless renovation, open trench and trenchless spot repair, inspection, and hydraulic cleaning of sewer pipes. Carbon footprint assessment was carried out according to the ISO14067:2018 standard. The life cycle stages considered included raw material extraction, manufacturing processes, maintenance/rehabilitation, and the transportation of materials. The functional unit was defined as: ‘A gravity sewer section with a length of 45 metres and a diameter of 200 to 1500 mm operating in normal conditions over a period of 100 years in the Netherlands’. A sensitivity analysis was included to assess how the results vary as a consequence of changes in the input dimensions. The findings of this study suggest that the main opportunities for reducing the environmental impacts of conventional, open trench pipe replacement lie in the choice of the pipe material and the design of the asphalt pavement that requires reinstatement. Adopting trenchless sewer rehabilitation technologies can significantly reduce the environmental burdens of sewer system rehabilitation.

An automatic defect classification and segmentation method on three-dimensional point clouds for sewer pipes

With the development of deep learning (DL), sewer pipe inspection on two-dimensional (2D) images has achieved remarkable accuracy. However, extracting defect measurements from these 2D images is challenging due to the curved nature of pipes and the lack of depth information. Point clouds can restore the three-dimensional (3D) information of objects. To effectively identify defects in disordered and sparse point clouds, a 3D sewer pipe classification and segmentation method was proposed. In the encoder, the original point clouds are sampled and grouped and the local features in the clusters are extracted by two symmetric functions (1 × 1 convolution and the maximization function) to process the points with permutation invariance. In the decoder, the multi-scaling abstract features are upsampled using feature pyramid network (FPN) to predict the category of each point. Especially, the network structure and training strategy of the inspection method is optimized to improve the inspection accuracy. Furthermore, two data augmentation methods, namely random scaling and point jitter, are used to increase the data volume. An ablation experiment shows that the optimization of network structure can effectively improve the performance of the inspection model and the novel training strategies can stabilize the training process and prevent overfitting. Comparison among the state-of-the-art networks demonstrates that the proposed segmentation model attains the highest mIoU of 94.15 %, which is improved by 11.46 % with the optimization of network structure and training strategy. For the classification task, the F1 score and accuracy of the established model are 6.79 % and 5.46 % higher than PointNet++, respectively. These results signify the high-accuracy defect inspection capability of our proposed method on 3D point clouds of sewer pipelines.

Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques

Regular inspection of sewer pipes can detect serious defects in time, which is significant to ensure the healthy operation of sewer systems and urban safety. Currently, the widely used closed-circuit television (CCTV) inspection system relies mainly on manual assessment, which is labor intensive and inefficient. Therefore, it is urgent to develop an efficient and accurate automatic defect detection method. In this paper, an improved method based on YOLOv4 is proposed for the detection of sewer defects. A significant improvement of this method is using the spatial pyramid pooling (SPP) module to expand the receptive field and improve the ability of the model to fuse context features in different receptive fields. Meanwhile, the influence of three bounding box loss functions on model performance are compared based on their processing speed and detection accuracy, and the effectiveness of the combination of DIoU loss function and SPP module is verified. In addition, to address the lack of datasets for sewer defect detection, a dataset that contains 2700 images and 4 types of defects was created, which provides useful help for the application of computer vision techniques in this field. Experimental results show that, compared with the YOLOv4 model, the mean average precision (mAP) of the improved model for sewer defect detection are improved by 4.6%, the mAP can reach 92.3% and the recall can reach 89.0%. The improved model can effectively improve the detection and classification accuracy of sewer defects, and has significant advantages compared with other methods.

Autonomous control for miniaturized mobile robots in unknown pipe networks.

Despite recent advances in robotic technology, sewer pipe inspection is still limited to conventional approaches that use cable-tethered robots. Such commercially available tethered robots lack autonomy, and their operation must be manually controlled via their tethered cables. Consequently, they can only travel to a certain distance in pipe, cannot access small-diameter pipes, and their deployment incurs high costs for highly skilled operators. In this paper, we introduce a miniaturised mobile robot for pipe inspection. We present an autonomous control strategy for this robot that is effective, stable, and requires only low-computational resources. The robots used here can access pipes as small as 75 mm in diameter. Due to their small size, low carrying capacity, and limited battery supply, our robots can only carry simple sensors, a small processor, and miniature wheel-legs for locomotion. Yet, our control method is able to compensate for these limitations. We demonstrate fully autonomous robot mobility in a sewer pipe network, without any visual aid or power-hungry image processing. The control algorithm allows the robot to correctly recognise each local network configuration, and to make appropriate decisions accordingly. The control strategy was tested using the physical micro robot in a laboratory pipe network. In both simulation and experiment, the robot autonomously and exhaustively explored an unknown pipe network without missing any pipe section while avoiding obstacles. This is a significant advance towards fully autonomous inspection robot systems for sewer pipe networks.

The monitoring approaches and non-destructive testing technologies for sewer pipelines.

Sewage pipe deterioration has been one of the factors causing huge asset losses and urban hazards worldwide. For more efficient and reliable management, a variety of monitoring and non-destructive testing techniques has been developed for defect inspection and condition assessment of sewer pipes. In the present paper, the monitoring approaches of sewage pipes in the form of operational monitoring, structural monitoring, and durability monitoring are outlined. The fundamentals and features of various non-destructive testing (NDT) techniques for different target detect locations are presented. The stereo vision, light detection and ranging (LiDAR), and laser 3D scanning technologies that might serve to architect the digital twin of the pipeline are also described. What's more, the capabilities and limitations of these technologies are discussed and summarized in tables. Some possible visions for the development of inspection and quantitative evaluation of sewage pipes are also discussed. In practice, it is suggested that visual inspection techniques are the most feasible for the evaluation of underground pipes. In terms of quantitative and automated evaluation, visual inspection robots equipped with stereo vision or laser 3D scanning technology are the most promising.

Prioritizing inspection of sewer pipes based on self-cleansing criteria

ABSTRACT This paper aims to develop a sediment deposits hydraulic deterioration model based on self-cleansing criteria to prioritize the inspection of sewer systems. The model was trained with benchmarking literature values from earlier experiments and validated with household connections complaints data from Bogotá, Colombia. Recursive Feature Elimination with Cross-Validation (RFECV) and Bayesian Optimization (BO) were used to construct a Random Forest (RF) model to predict, at pipe level, the likelihood for a pipe to present sediment deposits. To evaluate the model’s prediction accuracy, two different performance indicators were used: (i) the Percentage of Effective Inspections, and (ii) Pipes per Inspection with sediments. The sediment deposits hydraulic deterioration model shows good overall performance with buffer zones radiuses of 250 m predicting which pipes tend to present sediment deposits over time. This model improves the understanding of sediment deposits in hydraulic deterioration models and can be used to prioritize inspection of sewer systems.

Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data

More frequent and thorough inspection of sewer pipes has the potential to save billions in utilities. However, the amount and quality of inspection are impeded by an imprecise and highly subjective manual process. It involves technicians judging stretches of sewer based on video from remote-controlled robots. Determining the state of sewer pipes based on these videos entails a great deal of ambiguity. Furthermore, the frequency with which the different defects occur differs a lot, leading to highly imbalanced datasets. Such datasets represent a poor basis for automating the labeling process using supervised learning. With this paper we explore the potential of self-supervision as a method for reducing the need for large numbers of well-balanced labels. First, our models learn to represent the data distribution using more than a million unlabeled images, then a small number of labeled examples are used to learn a mapping from the learned representations to a relevant target variable, in this case, water level. We choose a convolutional Autoencoder, a Variational Autoencoder and a Vector-Quantised Variational Autoencoder as the basis for our experiments. The best representations are shown to be learned by the classic Autoencoder with the Multi-Layer Perceptron achieving a Mean Absolute Error of 9.93. This is an improvement of 9.62 over the fully supervised baseline.

Development of Efficient Detection System Based on Artificial Intelligence for Inspection of Sewer Pipes

Development of Efficient Detection System Based on Artificial Intelligence for Inspection of Sewer Pipes

Sewer pipe defect detection via deep learning with local and global feature fusion

The damages triggered by the long-term corrosion, external disturbance, and uneven support pressure result in defective states of sewer pipes. Nowadays, the sewer pipe images are easily captured by closed-circuit televisions (CCTV) and quick-view (QV). However, the defect detection and grading still depend on human visual interpretation mainly, which are time-consuming, subjective and costing. Therefore, an efficient, accurate and automated method for sewer pipe defect localization and fine-grained classification is needed. To this end, this work introduces a novel two-stage learning-based method, to endow the capability of object detection network for the sewer pipe defect detection and fine-grained classification simultaneously, over all defect regions by exploiting the multi-layer global feature fusion techniques. Specifically, based on a two-stage object detection network, the strengthened region proposal network (SRPN) first generates representative region proposals by fusing multi-scale feature maps from the backbone network for defect region localization. Instead of only extracting the proposed regions for defect classification, we concatenate the proposed region feature and the global contextual feature from the corresponding image, to enhance feature representation of the fine-grained defect classification network. Extensive experiments demonstrate that our learning-based method gets the state-of-the-art performance for sewer pipe defect localization and fine-grained classification, compared with the several classical learning-based methods, according to the solid experiments. Furthermore, our method has been applied for sewer pipe inspection in many China cities.

Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective

The escalating number of aging sewer pipes necessitates an infrastructure asset management approach to achieve an efficient budget allocation for maintenance. This study suggests a risk-based prioritization framework for sewer pipe inspection considering the predicted condition of sewer pipes and the criticality of the economic, social and environmental impacts associated with them. The results from both models can be used to evaluate the risk of sewer pipes by classification into risk groups. A risk matrix is used for the classification, and it divides the sewer pipes into five risk groups. The results of this study show an improvement in the accuracy of finding sewer pipes in a bad condition using this framework. The condition prediction model can successfully find sewer pipes with a bad condition with over 70% precision. High-risk sewer pipes are highlighted using the differences in the environmental features as well as in the physical features associated with other sewer pipes. Additionally, through the combination of both the condition and criticality of sewer pipes, the framework systemically prioritizes needed maintenance for sewer pipes with a very bad condition. This prioritization framework is expected to help the process of deciding which sewer pipes should be prioritized within a constrained budget.

Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning

Computer vision techniques are widely studied for automating the interpretation of sewer pipe inspection videos, yet previous studies mainly focus on defect detection and segmentation of individual images, which cannot identify if the defect is the same one across consecutive video frames (i.e. track the defect). Nevertheless, the number of unique defects in the video is required for evaluating the pipe condition. This paper proposes a framework for tracking multiple sewer defects in CCTV videos based on defect detection and metric learning. First, a deep learning -based defect detection model and a metric learning model is developed and trained respectively using with our sewer datasets. Then, using the detections and their features from the trained models as inputs, the tracking module predicts tracks by Kalman filter and associates tracks based on defect motion, appearance features, and defect types. Our experiments demonstrate the framework is able to track sewer defects in CCTV videos with a decent IDF1 score of 57.4%. We notice that tracking performance can be influenced by the detection accuracy and configurations of the metric learning module. By analyzing the tracking results based on different weights of the distance metrics, we find that assigning larger weights to appearance and defect class distance metrics tends to increase IDF1 score, while larger motion distance weight may degrade tracking accuracy. The proposed framework contributes by tracking multiple sewer defects, which can assist with counting unique defects in inspection videos.

МОДЕЛЬ І МЕТОД НАВЧАННЯ КЛАСИФІКАТОРА КОНТЕКСТІВ СПОСТЕРЕЖЕННЯ НА ЗОБРАЖЕННЯХ ВІДЕОІНСПЕКЦІЇ СТІЧНИХ ТРУБ

Video inspection is often used to diagnose sewer pipe defects. To correctly encode founded defects according to existing standards, it is necessary to consider a lot of contextual information about the orientation and location of the camera from sewer pipe video inspection. A model for the classification of context on frames during observations in the video inspection of sewer pipes and a five-stage method of machine learning is proposed. The main idea of the proposed approach is to combine the methods of deep machine learning with the principles of information maximization and coding with self-correcting Hamming codes. The proposed model consists of a deep convolutional neural network with a sigmoid layer followed by the rounding output layer and information-extreme decision rules. The first stages of the method are data augmentation and training of the feature extractor in the Siamese model with softmax triplet loss function. The next steps involve calculating a binary code for each class of recognition that is used as a label in learning with a binary cross-entropy loss function to increase the compactness of the distribution of each class's observations in the Hamming binary space. At the last stage of the training method, it is supposed to optimize the parameters of radial-basis decision rules in the Hamming space for each class according to the existing information-extreme criterion. The information criterion, expressed as a logarithmic function of the accuracy characteristics of the decision rules, provides the maximum generalization and reliability of the model under the most difficult conditions in the statistical sense. The effectiveness of this approach was tested on data provided by Ace Pipe Cleaning (Kansas City, USA) and MPWiK (Wroclaw, Poland) by comparing learning results according to the proposed and traditional models and training schemes. The obtained model of the image frame classifier provides acceptable for practical use classification accuracy on the test sample, which is 96.8 % and exceeds the result of the traditional scheme of training with the softmax output layer by 6.8 %.

Data-Driven Framework for Modeling Productivity of Closed-Circuit Television Recording Process for Sewer Pipes

AbstractClosed-circuit television (CCTV) is widely used in North America for sewer pipe inspection due to several benefits, such as easy operation and lower upfront costs. To be useful, video foota...

Application of UAV for Sewer Pipe Inspection

Application of UAV for Sewer Pipe Inspection

Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

Abstract Sanitary sewer systems are designed to collect and transport sanitary wastewater and stormwater. Pipe inspection is important in identifying both the type and location of pipe defects to maintain the normal sewer operations. Closed-circuit television (CCTV) has been commonly utilized for sewer pipe inspection. Currently, interpretation of the CCTV images is mostly conducted manually to identify the defect type and location, which is time-consuming, labor-intensive and inaccurate. Conventional computer vision techniques are explored for automated interpretation of CCTV images, but such process requires large amount of image pre-processing and the design of complex feature extractor for certain cases. In this study, an automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN). The detection model is trained using 3000 images collected from CCTV inspection videos of sewer pipes. After training, the model is evaluated in terms of detection accuracy and computation cost using mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with high accuracy and fast speed. In addition, a new model is constructed and several hyper-parameters are adjusted to study the influential factors of the proposed approach. The experiment results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance. Specifically, the increase of dataset size and convolutional layers can improve the model accuracy. The adjustment of hyper-parameters such as filter dimensions or stride values contributes to higher detection accuracy, achieving an mAP of 83%. The study lays the foundation for applying deep learning techniques in sewer pipe defect detection as well as addressing similar issues for construction and facility management.

Approaches to Practical Sewer Pipe Inspection Technology Using Drones, Based on Public-Private-Academic Partnership

Approaches to Practical Sewer Pipe Inspection Technology Using Drones, Based on Public-Private-Academic Partnership

Development of Contraction Force Control System of Peristaltic Crawling Robot for Long-distance and Complex-line Sewer Pipe Inspection

Development of Contraction Force Control System of Peristaltic Crawling Robot for Long-distance and Complex-line Sewer Pipe Inspection

A technology for sewer pipe inspection (part 1): Design, calibration, corrections and potential application of a laser profiler

Abstract In order to ensure and extend the serviceability of sewage infrastructure, sewer managers need more accurate data to estimate its actual state. For that purpose, various inspection techniques can be implemented. A first prototype of an unbiased laser profiler was developed to improve the accuracy of collected data (Clemens, Stanic, Van der Schoot, Langeveld, Lepot, 2015). However, a higher accuracy needs to be achieved. This article presents the results of an improved prototype that delivers an increase in the measuring accuracy of 5–10 times, which was achieved by using more accurate equipment (lasers and cameras) and by improvements in the methodologies applied (e.g. removing the last bias image distortion by camera calibration). The new design presented hereafter provides accurate measurements (σ