Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system

Lettsome, Clyde A.; Tsai, Yichang; Kaul, Vivek

doi:10.1117/1.jei.21.4.043008

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…We discard the fully connected layers and the fifth pooling layer for the following reasons. (1) We expect meaningful side outputs to have different scales. The output plane of the layer after the fifth pooling is too small, which makes the interpolated prediction feature graph too fuzzy to generate fine results.…”

Section: Model Architecturementioning

confidence: 99%

“…With the development of computer vision techniques, 1 – 14 most researchers have focused on image processing methods to detect specific structural deterioration, such as bridge cracks, concrete spalling, steel cracks, road pavement cracks, and underground concrete pipe cracks. Considering the influence of noise on crack detection, Hu and Zhao 15 proposed a local binary patterns (LBP) method for crack detection.…”

Section: Introductionmentioning

confidence: 99%

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Method of convolutional neural network with hybrid attention for crack detection

Chen

2022

J. Electron. Imag.

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. In recent years, automatic image crack detection has become a critical task for ensuring the safety of various facilities. Many researchers utilized convolutional neural networks (CNNs) for crack detection. However, the existing CNN methods have the limitation of the receptive fields and struggle to establish long dependencies and global background information. Aiming to address this problem, we propose a CNN method with an attention module and recurrent mechanism. In terms of the backbone network, we propose a backbone network named Attention-VGG-19. In our network, we remove the last three fully connected layers of VGG-19 to expect the meaningful side output with different scales and to reduce the number of parameters. The fully connected layers are computationally intensive. The proposed network also provides integrated direct supervision for features of each convolutional stage. Then, to promote important features and suppress unimportant features, an attention module called CrackP attention is proposed. In the recurrent mechanism, the module can be unrolled into R loops to connect the path between one pixel and its neighboring ones. To prove the effectiveness of our proposed method, we evaluate it on four public crack datasets, DeepCrack, crack forest dataset (CFD), Crack500, and CrackTree260, on which it achieves F-score (F1) values of 0.876, 0.652, 0.692, and 0.593, respectively. After a guided filter is added, our method achieves F1 values of 0.878, 0.638, 0.681, and 0.526 on these four datasets.

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Section: Model Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method of convolutional neural network with hybrid attention for crack detection

Chen

2022

J. Electron. Imag.

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“…Genetic algorithms have been used for determining the threshold values in crack segmentation [7]; empirical mode decomposition (EMD) has been used to divide the complete spatial and frequency characteristic of the image features into different components [8]; filtered images have been used for crack segmentation [4,9]; and anisotropic methods for crack segmentation in different textured roads, such as Free-Form Anisotropy (FFA) [10], have also been developed, as well as filters based on the entropy of the patterns of intensities in image regions [11].…”

Section: Introductionmentioning

confidence: 99%

Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques

Medina¹,

Llamas²,

Zalama

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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In this paper, a comprehensive automatic visual inspection system for detecting pavement cracks, built around a Laser Road Inspection System (LRIS) onboard an instrumented vehicle, is presented. Two inertial profilers, a Differential Global Position System (DGPS), a high-definition camera and a high-speed area scan camera are the additional acquisition equipment. Visual appearance and geometrical information are obtained simultaneously since 3D profiles are obtained by capturing the laser line projected by the LRIS with the external area scan camera. Using AdaBoost algorithm for the combination of the processing results of these two types of data allows us to improve surface crack detection rates.

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Automatic Detection and Its Applications in Infrastructure

2022

Automation and Computational Intelligence for Road Maintenance and Management

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Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system

Cited by 10 publications

References 26 publications

Method of convolutional neural network with hybrid attention for crack detection

Method of convolutional neural network with hybrid attention for crack detection

Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques

Automatic Detection and Its Applications in Infrastructure

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