Intelligent corrosion detection and rating based on faster region-based convolutional neural network

Corrosion is a major defect in structural systems that has a significant economic impact and can pose safety risks if left untended. Currently, an inspector visually assesses the condition of a structure to identify corrosion. This approach is time-consuming, tedious, and subjective. Robotic systems, such as unmanned aerial vehicles, paired with computer vision algorithms have the potential to perform autonomous damage detection that can significantly decrease inspection time and lead to more frequent and objective inspections. This study evaluates the use of convolutional neural networks for corrosion detection. A convolutional neural network learns the appropriate classification features that in traditional algorithms were hand-engineered. Eliminating the need for dependence on prior knowledge and human effort in designing features is a major advantage of convolutional neural networks. This article presents different convolutional neural network–based approaches for corrosion assessment on metallic surfaces. The effect of different color spaces, sliding window sizes, and convolutional neural network architectures are discussed. To this end, the performance of two pretrained state-of-the-art convolutional neural network architectures as well as two proposed convolutional neural network architectures are evaluated, and it is shown that convolutional neural networks outperform state-of-the-art vision-based corrosion detection approaches that are developed based on texture and color analysis using a simple multilayered perceptron network. Furthermore, it is shown that one of the proposed convolutional neural networks significantly improves the computational time in contrast with state-of-the-art pretrained convolutional neural networks while maintaining comparable performance for corrosion detection.

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Steel bridge corrosion inspection with combined vision and thermographic images

Structural Health Monitoring ◽

10.1177/1475921721989407 ◽

2021 ◽

pp. 147592172198940

Author(s):

Hyung Jin Lim ◽

Soonkyu Hwang ◽

Hyeonjin Kim ◽

Hoon Sohn

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Imaging System ◽

Three Dimensional ◽

Steel Bridge ◽

Two Dimensional ◽

Infrared Images ◽

Seamless Integration ◽

One Dimensional ◽

Corrosion Detection

In this study, a faster region-based convolutional neural network is constructed and applied to the combined vision and thermographic images for automated detection and classification of surface and subsurface corrosion in steel bridges. First, a hybrid imaging system is developed for the seamless integration of vision and infrared images. Herein, a three-dimensional red/green/blue vision image is obtained with a vision camera, and a one-dimensional active infrared (IR) amplitude image is obtained from the infrared camera for temperature measurements with halogen lamps as the heat source. Subsequently, the three-dimensional red/green/blue vision image is converted to a two-dimensional chroma blue- and red-difference (CbCr) image because the CbCr image is known to be more sensitive to surface corrosion than the red/green/blue image. A combined three-dimensional (CbCr-IR) image is then constructed by fusing the two-dimensional CbCr image and the one-dimensional infrared image. For the automated corrosion detection and classification, a faster region-based convolutional neural network is constructed and trained using the combined three-dimensional CbCr-IR images of surface and subsurface corrosion on steel bridge structures. Finally, the performance of the trained, faster region-based convolutional neural network is evaluated using the images acquired from real bridges and compared with faster region-based convolutional neural networks trained by other vision and IR-based images. The uniqueness of this study is attributed to the (1) corrosion detection reliability improvements based on the fusion of vision and infrared images, (2) automated corrosion detection and classification with a faster region-based convolutional neural network, (3) detection of subsurface corrosion that is not detectable using vision images only, and (4) application to field bridge inspection.

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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No-Reference Utility Estimation with a Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.09.iriacv-202 ◽

2018 ◽

Vol 2018 (9) ◽

pp. 202-1-202-6 ◽

Cited By ~ 2

Author(s):

Edward T. Scott ◽

Sheila S. Hemami

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Utility Estimation

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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