Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

Hoang, Nhat‐Duc; Tran, Van-Duc

doi:10.1155/2019/8097213

Cited by 60 publications

(19 citation statements)

References 54 publications

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“…erefore, image texture analysis used for extracting the coarseness of image regions is helpful to recognize them. Texture descriptors [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] have been proved to be highly useful for image classification in various fields. In this study, the highly discriminative local ternary pattern is employed.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.

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Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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“…Another support vector machine (SVM) approach was applied by Hoang et al [54] for image processing‐based detection of pipe corrosion. The image texture including statistical measurements of image colors, gray‐level co‐occurrence matrix, and gray‐level run length is employed to extract features of the pipe surface.…”

Section: State Of the Artmentioning

confidence: 99%

Machine learning for material characterization with an application for predicting mechanical properties

Stoll

Benner

2021

GAMM-Mitteilungen

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Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data‐driven methods for the discovery of patterns among multiple lengthscales and time‐scales and structure‐property relationships essential. These data‐driven approaches show enormous promise within materials science. The following review covers machine learning (ML) applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of ML methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness, or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests, or creep tests are often time consuming and expensive to perform. Therefore, the application of ML approaches is considered helpful for an easier generation of material property information. This study also gives an application of ML methods on small punch test (SPT) data for the determination of the property ultimate tensile strength for various materials. A strong correlation between SPT data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with ML.

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“…Shapes and sizes of corrosions were applied to detect the pitting corrosion in Pereira et al (2012). The texture analysis for corrosion detection was proposed in Hoang and Tran (2019). In their theory, based on image colour, gray-level co-occurrence matrix (GLCM) and gray-level run lengths (GLRL), 78 features were extracted from the corrosion area.…”

Section: Low-level Feature-based Corrosion Detectorsmentioning

confidence: 99%

AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

Yang

Luo

et al. 2021

J Ambient Intell Human Comput

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Corrosion has been concerned as a serious safety issue for metallic facilities. Visual inspection carried out by an engineer is expensive, subjective and time-consuming. Micro Aerial Vehicles (MAVs) equipped with detection algorithms have the potential to perform safer and much more efficient visual inspection tasks than engineers. Towards corrosion detection algorithms, convolution neural networks (CNNs) have enabled the power for high accuracy metallic corrosion detection. However, these detectors are restricted by MAVs on-board capabilities. In this study, based on You Only Look Once v3-tiny (Yolov3-tiny), an accurate deep learning-based metallic corrosion detector (AMCD) is proposed for MAVs on-board metallic corrosion detection. Specifically, a backbone with depthwise separable convolution (DSConv) layers is designed to realise efficient corrosion detection. The convolutional block attention module (CBAM), three-scale object detection and focal loss are incorporated to improve the detection accuracy. Moreover, the spatial pyramid pooling (SPP) module is improved to fuse local features for further improvement of detection accuracy. A field inspection image dataset labelled with four types of corrosions (the nubby corrosion, bar corrosion, exfoliation and fastener corrosion) is utilised for training and testing the AMCD. Test results show that the AMCD achieves 84.96% mean average precision (mAP), which outperforms other state-of-the-art detectors. Meanwhile, 20.18 frames per second (FPS) is achieved leveraging NVIDIA Jetson TX2, the most popular MAVs on-board computer, and the model size is only 6.1 MB.

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Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

Cited by 60 publications

References 54 publications

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Machine learning for material characterization with an application for predicting mechanical properties

AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

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