Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification

Bhattacharya, Gaurab; Mandal, Bappaditya; Puhan, Niladri B.

doi:10.1109/tcsvt.2020.3028008

Cited by 20 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Use of visual attention in concrete defect classification is however limited; except a recent work [47] involving residual attention mechanism. However, the considerable amount of parameters and computations involved in parallel feature extraction make the network inferior for real-time applications.…”

Section: Attention Mechanismmentioning

confidence: 99%

“…Similarly, we perform spatial squeezing operation inside simultaneous excitation module using 1 × 1 convolution, rather than using the pooling operation in [22], [23] to adaptively generate local spatial descriptors. Moreover, the iDAAM architecture attributes to significant reduction in the number of parameters compared to the state-of-the-art [47] due to the absence of explicit multi-branch feature extraction.…”

Section: Attention Mechanismmentioning

confidence: 99%

“…The literature search dictates that the researchers have rarely worked with overlapping defect classes (such as spallation leading to exposed bar, which often leads to/co-exist with corrosion), which are often encountered in real-world applications with the challenging appearance variations, as discussed earlier. Recently, [16], [47] analyzed overlapping multi-class defects in CODEBRIM dataset using reinforcement learning and attention augmented CNN, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification

Bhattacharya

Mandal

Puhan

2021

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and painting, along with uncontrolled weather conditions and illuminations. In this paper, we propose an interleaved deep artifacts-aware attention mechanism (iDAAM) to classify multitarget multi-class and single-class defects from structural defect images. Our novel architecture is composed of interleaved finegrained dense modules (FGDM) and concurrent dual attention modules (CDAM) to extract local discriminative features from concrete defect images. FGDM helps to aggregate multi-layer robust information with wide range of scales to describe visuallysimilar overlapping defects. On the other hand, CDAM selects multiple representations of highly localized overlapping defect features and encodes the crucial spatial regions from discriminative channels to address variations in texture, viewing angle, shape and size of overlapping defect classes. Within iDAAM, FGDM and CDAM are interleaved to extract salient discriminative features from multiple scales by constructing an end-toend trainable network without any preprocessing steps, making the process fully automatic. Experimental results and extensive ablation studies on three publicly available large concrete defect datasets show that our proposed approach outperforms the current state-of-the-art methodologies.

show abstract

Section: Attention Mechanismmentioning

confidence: 99%

Section: Attention Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification

Bhattacharya

Mandal

Puhan

2021

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Thus, it is important to construct a model that takes into account the practical inspection process, and various studies have been conducted focusing on the above first point [13][14][15][16][17]. For example, in the literature [16], the global average pooling layer is inserted into the originally constructed model, and the class activation map proposed in the field of general object recognition [18][19][20][21] is used to reveal the regions that the model paid attention to during the estimation.…”

Section: Introductionmentioning

confidence: 99%

Reliable Estimation of Deterioration Levels via Late Fusion Using Multi-View Distress Images for Practical Inspection

Maeda

Ogawa

et al. 2021

J. Imaging

View full text Add to dashboard Cite

This paper presents reliable estimation of deterioration levels via late fusion using multi-view distress images for practical inspection. The proposed method simultaneously solves the following two problems that are necessary to support the practical inspection. Since maintenance of infrastructures requires a high level of safety and reliability, this paper proposes a neural network that can generate an attention map from distress images and text data acquired during the inspection. Thus, deterioration level estimation with high interpretability can be realized. In addition, since multi-view distress images are taken for single distress during the actual inspection, it is necessary to estimate the final result from these images. Therefore, the proposed method integrates estimation results obtained from the multi-view images via the late fusion and can derive an appropriate result considering all the images. To the best of our knowledge, no method has been proposed to solve these problems simultaneously, and this achievement is the biggest contribution of this paper. In this paper, we confirm the effectiveness of the proposed method by conducting experiments using data acquired during the actual inspection.

show abstract

“…The presented method used five specific parameters, concrete samples, and acquired images. Multitarget multiclass defects in concrete structures found in civil infrastructures were classified using a deep multi-deformation-aware attention learning architecture, comprising a multiscale assembly of attention and fine-grained feature-induced attention modules [11]. An image-processing technique to provide concrete regression analysis has been employed [12]; the technique can be an auxiliary tool for destructive and nondestructive testing methods.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Kim

Choi

et al. 2021

Sensors

View full text Add to dashboard Cite

With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.

show abstract

Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification

Cited by 20 publications

References 24 publications

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification

Reliable Estimation of Deterioration Levels via Late Fusion Using Multi-View Distress Images for Practical Inspection

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Contact Info

Product

Resources

About