Integrated Vision-Based System for Automated Defect Detection in Sewer Closed Circuit Television Inspection Videos

Halfawy, Mahmoud R.; Hengmeechai, Jantira

doi:10.1061/(asce)cp.1943-5487.0000312

Cited by 42 publications

(16 citation statements)

References 24 publications

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“…Sinha [1] and Fieguth [2] used mathematical morphology method to preprocess the image, then extracted the texture, length, roundness and other shape features of the pipeline, and used K-NN algorithm to detect crack defects, pipe joint defects and pore corrosion. Halfawy and Hengmeechai [3,4] proposed a morphological method, which used differences in brightness of objects in focus as a basis to segment ROIs. Their method used histograms of oriented gradients (HOG) and an SVM classifier, trained with 1000 images to classify the ROIs as containing or not containing defects.…”

Section: Related Workmentioning

confidence: 99%

Research on Pipeline Defect Detection Based on Optimized Faster R-cnn Algorithm

Zhang¹,

Li²,

Lv³

et al. 2019

dtcse

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Aiming at the problem of low efficiency and high labor intensity in manual inspection of drainage pipes, a method of defect inspection of drainage pipes based on optimized Faster-Rcnn algorithm is proposed. Faster R-CNN is an algorithm proposed for target detection this year, which is based on the deep learning network model of region-based recommendation network (RPN). This paper analyzes the implementation of RPN network in Faster R-CNN algorithm, optimizes the network and introduces K-Means clustering method. By clustering all Anchors in the training set and inputting the clustering results into the RPN network, the training of the network can be accelerated and the recognition accuracy of the algorithm can be improved. The experimental results show that the accuracy of the algorithm is up to 92.4%, which has great application value. This research has important reference significance to promote the automatic detection of sewage pipeline defects.

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Section: Related Workmentioning

confidence: 99%

Research on Pipeline Defect Detection Based on Optimized Faster R-cnn Algorithm

Zhang¹,

Li²,

Lv³

et al. 2019

dtcse

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show abstract

“…Liu et al [126] proposed a method for automated surface crack monitoring and assessment of concrete structures based on adaptive digital image processing. Halfawy and Hengmeechai [127] embedded the visionbased defection recognition system into the closed circuit television (CCTV) system mounted in the sewer to inspect its defections automatically. Adhikari et al [128] presented an approach of automated condition assessment of concrete bridges based on digital image analyses.…”

Section: Crack Inspection and Characterizationmentioning

confidence: 99%

A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications

2016

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In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.

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“…Previous investigations using machine vision for crack detection have used image processing algorithms, such as Hough transform, wavelet transforms, region-based segmentation, Hessian matrix-based vesselness measurement technique, and principal component analysis to identify patterns in images that indicate cracks [4][5][6]. More recently, machine learning and deep learning algorithms to identify cracks have also become a popular method of crack detection [7][8][9][10]. Convolutional neural networks (CNNs), which are deep learning techniques, excel in object recognition applications because they are excellent at extracting features from large data sets that make adept machine learning models for object recognition [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Choi

Bell

Kim

et al. 2021

Sensors

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Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

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Integrated Vision-Based System for Automated Defect Detection in Sewer Closed Circuit Television Inspection Videos

Cited by 42 publications

References 24 publications

Research on Pipeline Defect Detection Based on Optimized Faster R-cnn Algorithm

Research on Pipeline Defect Detection Based on Optimized Faster R-cnn Algorithm

A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications

UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

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