A New Image-Based Model For Predicting Cracks In Sewer Pipes

Khalifa, Iraky; Aboutabl, Amal Elsayed; Barakat, Gamal Sayed AbdelAziz

doi:10.14569/ijacsa.2013.041210

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Mean, Gaussian, and other fltering operations are employed to reduce image noise [36]. Furthermore, morphological [37] methods are used to process pipeline crack images, enabling the determination of parameters such as crack length and width through pixel resolution calculation [38] and the estimation of severity. Using the above method to operate sequences and segment pipeline defects based on diferent types of images provides a feasible approach for processing a large number of original images.…”

Section: Pipeline Detection Methods Based On Traditional Machinementioning

confidence: 99%

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

Luo,

Du,

Niu

2024

Structural Control and Health Monitoring

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During the process of urban development, there is large-scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, accurately identify, and precise prediction of the condition of urban subterranean drainage networks. The development process of detection and evaluation methods for underground concrete drainage pipe networks is the focus of this study. The study discusses common algorithms for classifying, locating, and quantifying pipeline defects by combining the principles of deep learning with typical application examples. The intelligent progression of information collection methods, image processing techniques, damage prediction models, and pipeline diagnostic systems is systematically elaborated upon. Lastly, prospects for future research of intelligent pipeline diagnosis are provided.

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Section: Pipeline Detection Methods Based On Traditional Machinementioning

confidence: 99%

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

Luo,

Du,

Niu

2024

Structural Control and Health Monitoring

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“…Similarly, Ye et al [ 11 ] accomplish the classification of pipe defects by fusing multiple features of pipe images combined with machine learning algorithms. Khalifa et al [ 12 ] identify crack defects present in pipes by morphological methods after noise removal from the original images. This method can check for small cracks that cannot be judged by the naked eye, but the method cannot make judgments about other defects.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Defect Detection System for Petrochemical Pipeline Based on Cycle-GAN and YOLO v5

Chen

Li³

et al. 2022

Sensors

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Defect detection of petrochemical pipelines is an important task for industrial production safety. At present, pipeline defect detection mainly relies on closed circuit television method (CCTV) to take video of the pipeline inner wall and then detect the defective area manually, so the detection is very time-consuming and has a high rate of false and missed detections. To solve the above issues, we proposed an automatic defect detection system for petrochemical pipeline based on Cycle-GAN and improved YOLO v5. Firstly, in order to create the pipeline defect dataset, the original pipeline videos need pre-processing, which includes frame extraction, unfolding, illumination balancing, and image stitching to create coherent and tiled pipeline inner wall images. Secondly, aiming at the problems of small amount of samples and the imbalance of defect and non-defect classes, a sample enhancement strategy based on Cycle-GAN is proposed to generate defect images and expand the data set. Finally, in order to detect defective areas on the pipeline and improve the detection accuracy, a robust defect detection model based on improved YOLO v5 and Transformer attention mechanism is proposed, with the average precision and recall as 93.10% and 90.96%, and the F1-score as 0.920 on the test set. The proposed system can provide reference for operators in pipeline health inspection, improving the efficiency and accuracy of detection.

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“…Thus, the image resolution and the physical dimensions of the image can be related to determine the quantity of space that each pixel will represent in the image. Equations ( 6) and ( 7) are used to calculate the space contributed by each pixel in an image [32]. The pixel can be represented using the pixel width (P w ) and pixel height (P h ).…”

Section: Calculation Of Pixel Resolutionmentioning

confidence: 99%

In-Line Inspection of Pipeline Defects Detection Using Ring-Type Laser

Durai¹,

Hsu²,

Chang³

2021

WJET

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Pipeline plays a vital role in transporting fluids like oils, water, and petrochemical substances for longer distances. Based on the materials they carry, prolonged usage may cause the initiation of defects in the pipeline. These defects occur due to the formed salt deposits, chemical reaction happens between the inner surface and the transferring substance, prevailing environmental conditions, etc. These defects, if not identified earlier may lead to significant losses to the industry. In this work, an in-line inspection system utilizes the nondestructive way for analyzing the internal defects in the petrochemical pipeline. This system consists of a pipeline inspection robot having two major units namely the visual inspection unit and the power carrier unit. The visual inspection unit makes use of a ring-type laser diode and the camera. The laser diode serves as a light source for capturing good quality images of inspection. This unit is controlled by the Arduino in the power carrier unit which provides the necessary movement throughout the pipe. The inspected images captured by the camera are further processed with the aid of NI vision assistant software. After applying the processing function parameters provided by this software, the defect location can be clearly visualized with high precision. Three sets of defects are introduced in a Polylactide (PLA) pipe based on its position and angle along the circumference of the pipe. Further, this robot system serves as a real-time interactive image synchronization system for acquiring the inspected images. By comparing the actual and calculated defect size, the error percentage obtained was less than 5%.

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A New Image-Based Model For Predicting Cracks In Sewer Pipes

Cited by 6 publications

References 12 publications

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

An Automatic Defect Detection System for Petrochemical Pipeline Based on Cycle-GAN and YOLO v5

In-Line Inspection of Pipeline Defects Detection Using Ring-Type Laser

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