A Survey of Computer Vision Based Corrosion Detection Approaches

Ahuja, Sanjay Kumar; Shukla, Manoj Kumar

doi:10.1007/978-3-319-63645-0_6

Cited by 32 publications

(14 citation statements)

References 23 publications

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“…In recent years, the image processing based corrosion detection and analysis approaches have been carried out for inspections in various fields, particularly where access is not viable owing to environmental and other variables engaged [1]. According to the survey [2] on solutions to corrosion based on computer vision, most of the methods are based on image characteristics. The current methods for detecting and analyzing corrosion can be classified into various classifications such as:…”

Section: Related Workmentioning

confidence: 99%

Surface Corrosion Grade Classification using Convolution Neural Network

Ahuja¹,

Shukla²,

Ravulakollu³

2019

IJRTE

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 Abstract: Corrosion is a prevalent issue in the oil and gas industry. Usually, pipelines made of Iron are used for oil and gas transportation. The pipelines are large and distributed over big fields above the ground, underground and even underwater. Corrosion gets developed because of environmental variables such as temperature, humidity and acidic nature of the liquids. There are different techniques for detecting and monitoring corrosion development, both destructive and non-destructive. Visual inspection is a common technique of surface corrosion analysis, but manual inspection is extremely dependent on the inspecting person's abilities and expertise. The findings of the manual inspection are qualitative and may be biased, may result into the accidents because of incorrect analysis. Corrosion must be accurately detected in early phases to prevent unwanted accidents. This paper will present a computer vision-based approach in combination with deep learning for corrosion classification as perISO-8501 standard. The findings of the assessment are unbiased and in a fair acceptable range similar to the outcomes of the visual inspection.

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Section: Related Workmentioning

confidence: 99%

Surface Corrosion Grade Classification using Convolution Neural Network

Ahuja¹,

Shukla²,

Ravulakollu³

2019

IJRTE

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“…The CNN-based methods are generally derived from ANNs but display better learning of image features and require few parameters in comparison. This is mainly due to partial connections, weights, and pooling processes amid neurons [31]. Compared to traditional methods, the CNN-based crack detection framework does not require the pre-extraction and calculation of features [32].…”

Section: Introductionmentioning

confidence: 99%

Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages

Munawar

Ullah

Heravi

et al. 2021

Drones

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Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions.

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“…The vision processing method is capable of constructing a mosaic image, which can produce a metric representation of the areas inspected by the robot, from the multiple images captured from different positions of the robot. Many computer-vision based methods for detecting corrosion in metal structures, including ship hulls and steel bridges, have been developed [26]. According to the outcomes of the survey [26], the color is the principal representative feature for identifying the corroded areas.…”

Section: Introductionmentioning

confidence: 99%

“…Many computer-vision based methods for detecting corrosion in metal structures, including ship hulls and steel bridges, have been developed [26]. According to the outcomes of the survey [26], the color is the principal representative feature for identifying the corroded areas. Furthermore, the cited survey concludes that learning-based methods could perform better than the non-learning methods in detecting corrosion through vision.…”

Section: Introductionmentioning

confidence: 99%

A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls

Muthugala

Cruz

et al. 2020

Sensors

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Regular dry dock maintenance work on ship hulls is essential for maintaining the efficiency and sustainability of the shipping industry. Hydro blasting is one of the major processes of dry dock maintenance work, where human labor is extensively used. The conventional methods of maintenance work suffer from many shortcomings, and hence robotized solutions have been developed. This paper proposes a novel robotic system that can synthesize a benchmarking map for a previously blasted ship hull. A Self-Organizing Fuzzy logic (SOF) classifier has been developed to benchmark the blasting quality of a ship hull similar to blasting quality categorization done by human experts. Hornbill, a multipurpose inspection and maintenance robot intended for hydro blasting, benchmarking, and painting, has been developed by integrating the proposed SOF classifier. Moreover, an integrated system solution has been developed to improve dry dock maintenance of ship hulls. The proposed SOF classifier can achieve a mean accuracy of 0.9942 with an execution time of 8.42 µs. Realtime experimenting with the proposed robotic system has been conducted on a ship hull. This experiment confirms the ability of the proposed robotic system in synthesizing a benchmarking map that reveals the benchmarking quality of different areas of a previously blasted ship hull. This sort of a benchmarking map would be useful for ensuring the blasting quality as well as performing efficient spot wise reblasting before the painting. Therefore, the proposed robotic system could be utilized for improving the efficiency and quality of hydro blasting work on the ship hull maintenance industry.

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A Survey of Computer Vision Based Corrosion Detection Approaches

Cited by 32 publications

References 23 publications

Surface Corrosion Grade Classification using Convolution Neural Network

Surface Corrosion Grade Classification using Convolution Neural Network

Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages

A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls

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