Box-and-Ellipse-Based ANFIS for Bridge Coating Assessment

Chen, Po‐Han; Yang, Ya-Ching; Chang, Luh‐Maan

doi:10.1061/(asce)cp.1943-5487.0000041

Cited by 16 publications

(3 citation statements)

References 9 publications

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“…Although no work prior to that presented by German et al (2012) has been performed in the area of spalling detection and property retrieval, prior research efforts in rust detection and property retrieval are relevant due to the similarities in the two damage types (heavily chaotic and distinct in color). A myriad of methods use grayscale images to detect the existence of rust defects [NFRA (Chen and Chang 2006), BE-ANFIS (Chen et al 2010)]. In order to work in the gray-scale, the color image is converted for processing, causing loss of information which then results in miscalculation of uneven background spots as rust (Chen et al 2012).…”

Section: Concrete Spalling: Detection and Property Measurementsmentioning

confidence: 99%

Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures

Koch

Paal

Rashidi

et al. 2014

Advances in Structural Engineering

117

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Large concrete structures need to be inspected in order to assess their current physical and functional state, to predict future conditions, to support investment planning and decision making, and to allocate limited maintenance and rehabilitation resources. Current procedures in condition and safety assessment of large concrete structures are performed manually leading to subjective and unreliable results, costly and time-consuming data collection, and safety issues. To address these limitations, automated machine vision-based inspection procedures have increasingly been proposed by the research community. This paper presents current achievements and open challenges in vision-based inspection of large concrete structures. First, the general concept of Building Information Modeling is introduced. Then, vision-based 3D reconstruction and as-built spatial modeling of concrete civil infrastructure are presented. Following that, the focus is set on structural member recognition as well as on concrete damage detection and assessment exemplified for concrete columns. Although some challenges are still under investigation, it can be concluded that vision-based inspection methods have significantly improved over the last 10 years, and now, as-built spatial modeling as well as damage detection and assessment of large concrete structures have the potential to be fully automated.

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Section: Concrete Spalling: Detection and Property Measurementsmentioning

confidence: 99%

Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures

Koch

Paal

Rashidi

et al. 2014

Advances in Structural Engineering

117

View full text Add to dashboard Cite

show abstract

“…Lee et al developed an automated processor that can recognize the presence of bridge coating rust defects [13]. In order to solve the problem of nonuniform illumination, Chen et al proposed the adaptive ellipse approach (AEA), the box-and-ellipse-based adaptive-network-based fuzzy inference system (BE-ANFIS), and the support-vectormachine-based rust assessment approach (SVMRA) [14][15][16]. In order to adapt to various background colors and overcome the effects of background noise or nonuniform illumination, Shen et al proposed a rust defect recognition method based on color and texture feature, which combines the Fourier transform and color image processing [17].…”

Section: Introductionmentioning

confidence: 99%

Deep‐Learning‐Based Bughole Detection for Concrete Surface Image

Yao

Wei

Yang

et al. 2019

Advances in Civil Engineering

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Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time-consuming and difficult. This paper proposed a deep-learning-based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real-world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color-differences, and nonuniform illumination on the concrete surface.

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“…Although RIRAN is capable to describe gradually changed colors automatically, the rust segmentation is still based on K-Means algorithm, which is powerless to deal with images under environmental conditions such as nonuniform illuminations, low-contrast digital images, and noise on painting surface (Lee 2005). Instead of K-Meansalgorithm, it will be interesting to apply Box-and-Ellipse-Based ANFIS approach[16] or Support-Vector-Machine-Based Method[17] to recognize the defects and assess the rust ratio. Both methods have better performance than K-Means in non-uniform illumination conditions.…”

mentioning

confidence: 99%

A Rust Intensity Recognition Approach for Steel Bridge Surface Coating Images Based on Artificial Neural Networks

Chen¹,

Shen²,

Chang³

2011

28th International Symposium on Automation and Robotics in Construction (ISARC 2011)

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One of the traditional methods to assess the quality of the coating on the steel bridge surface is calculating the ratio of rust to the total area of coated surface. In North America, digital image processing has been applied on quality assessment of steel bridge coating since late 1990s due to the difficulty of rust area quantification by human vision. Most of the previously developed systems segment images into two groups, rust and background, before assess the rust ratio. These systems are usually lack of the ability to recognize the intensities of rust. In order to provide a flexible assessment, one pioneer method called adaptive ellipse approach (AEA) was proposed in 2009 to classify the images into three groups, background, rust, and mild-rust. However, even the three-group system is still weak at describing gradually changed colors and thus unable to classify those colors automatically. In this paper, we segmented the image into several clusters based on the similarity of colors, and used the root-mean-square standard deviation (RMSSTD) to measure the similarity of each cluster. Artificial Neural Networks was applied to choose the best number of clusters based on the values of RMSSTD. This paper models a rust intensity recognition approach to choose the most suitable number of clusters for describing rust intensities, trying to overcome a hurdle that two-or three-group systems could not resolve.

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Box-and-Ellipse-Based ANFIS for Bridge Coating Assessment

Cited by 16 publications

References 9 publications

Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures

Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures

Deep‐Learning‐Based Bughole Detection for Concrete Surface Image

A Rust Intensity Recognition Approach for Steel Bridge Surface Coating Images Based on Artificial Neural Networks

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