Underground sewer pipe condition assessment based on convolutional neural networks

Hassan, Syed Ibrahim; Dang, L. Minh; Mehmood, Irfan; Im, Sungbin; Choi, Cheol Woong; Kang, Jae-Mo; Park, Young-Soo; Moon, Hyeonjoon

doi:10.1016/j.autcon.2019.102849

Cited by 136 publications

(42 citation statements)

References 22 publications

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“…In water pipeline inspections, it is estimated that around 20% to 25% of defects are missed due to human errors, which are attributed to stress and difference in inspection worker skill levels [145][146][147][148][149][150][151]. In water pipeline inspections, AI-based defect detection rates were displayed to be comparable to human detection rates [145][146][147][148][149][150][151][152][153][154][155]. Deep learning is utilised in exterior WT inspections [22,45,80] and could be utilised in interior WTB inspections as well, having an AI classify, localize defects within the inspection frame and infer bounding boxes for the defects in the inspection footage.…”

Section: Damage Detectionmentioning

confidence: 99%

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

Kulsinskas,

Durdevic,

Ortiz-Arroyo

2021

Energies

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Interior and exterior wind turbine blade inspections are necessary to extend the lifetime of wind turbine generators. The use of unmanned vehicles is an alternative to exterior wind turbine blade inspections performed by technicians that require the use of cranes and ropes. Interior wind turbine blade inspections are even more challenging due to the confined spaces, lack of illumination, and the presence of potentially harmful internal structural components. Additionally, the cost of manned interior wind turbine blade inspections is a major limiting factor. This paper analyses all aspects of the viability of using manually controlled or autonomous aerial vehicles for interior wind turbine blade inspections. We discuss why the size, weight, and flight time of a vehicle, in addition to the structure of the wind turbine blade, are the main limiting factors in performing internal blade inspections. We also describe the design issues that must be considered to provide autonomy to unmanned vehicles and the control system, the sensors that can be used, and introduce some of the algorithms for localization, obstacle avoidance and path planning that are best suited for the task. Lastly, we briefly describe which non-destructive test instrumentation can be used for the purpose.

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Section: Damage Detectionmentioning

confidence: 99%

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

Kulsinskas,

Durdevic,

Ortiz-Arroyo

2021

Energies

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“…Wang and Cheng [12] proposed DilaSeg-CRF integrated dilated convolution and multiscale techniques with RNN layers for automatic severity assessment of sewer pipeline faults. Hassan et al [13] proposed a sewer fault classification system using CCTV imagery and convolutional neural networks (CNNs). The proposed system showed an accuracy of over 90%.…”

Section: Related Workmentioning

confidence: 99%

Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences

Guo

et al. 2020

IEEE Access

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Most existing sewer pipeline condition assessment methods determine the presence and types of faults via examination of videos, which is a time-consuming and labor-intensive process. A few automatic methods based on image processing techniques can be used to detect specific faults. However, these methods have limitations due to the presence of unpredictable sewer pipeline fault patterns. Deep learning methods have also been applied to sewer pipeline fault detection. However, these methods require a large amount of annotated data to obtain reliable results. In this paper, we propose a fault detection method that applies unsupervised machine learning based anomaly detection algorithms with feature extraction to videos recorded by new sewer pipeline visual inspection equipment. The recorded videos are regarded as sequence signals, which are converted into feature vectors, followed by application of an anomaly detection algorithm. Unlike existing methods, the proposed method is computationally efficient as it does not require an annotated fault sample database for training fault detection models. We evaluate various anomaly detection algorithms and feature combinations on real sewer pipeline data collected in Shenzhen, with an overall accuracy result of above 90%. The proposed method provides a new and fast technique for surveying urban sewer pipelines, and to facilitate further research in this area, we have made the code and data used in this paper publicly available.

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“…Early detection of sewer defects provides support to system maintenance and management, and is thus an essential step towards improved sewer reliability [4,6,7]. Nevertheless, the traditional manual visual inspection of sewer defects is labor-intensive, subjective and error-prone, which can hardly meet the longterm development requirements of the large and complex modern sewer systems [5,8,9]. Automated detection and diagnosis of sewer defects have become an urgent need and an important research direction [4,9,10].…”

Section: Introductionmentioning

confidence: 99%

Automated Sewer Defects Detection Using Style-Based Generative Adversarial Networks and Fine-Tuned Well-Known CNN Classifier

et al. 2021

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Automated sewer defects detection has become an important trend for better management and maintenance of urban sewer systems. Deep learning technology has developed rapidly and offers an innovative solution for automated detection in engineering applications. However, insufficient data and unbalanced samples have proposed a big challenge to deep learning model training. This study adopts the state-of-the-art Style-based Generative Adversarial Networks (StyleGANs) model and compares the performances of its two variants in producing high-quality synthetic sewer defects images. Seven wellknown CNN models are further fine-tuned and trained using the synthetic images for automated sewer defects detection to examine the effects of StyleGANs on augmenting the detection performance. Results show that both StyleGANs are efficient in producing high-quality images with various styles and high-level details for multiple types of sewer defects. Specifically, the StyleGAN2-Adaptive Discriminator Augmentation (StyleGAN2-ADA) with the aid of Freeze Discriminator (Freeze-D) yields the best model performance. Among the adopted CNN classifiers, Inception_v3 achieves the highest detection accuracy. The mean detection accuracy is 94% (with a specific accuracy of 99.7%, 97%, 95.3% and 84% for tree root, residential wall, disjoint and obstacle, respectively) and confirms the reliability of the StyleGANs' performance. The study shows that StyleGANs provide a promising method to alleviate the limited and uneven dataset problem and can improve the deep learning model performance.

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Underground sewer pipe condition assessment based on convolutional neural networks

Cited by 136 publications

References 22 publications

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences

Automated Sewer Defects Detection Using Style-Based Generative Adversarial Networks and Fine-Tuned Well-Known CNN Classifier

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