Recognition of asphalt pavement crack length using deep convolutional neural networks

Zheng, Tong; Gao, Jie; Han, Zhenqiang; Wang, Zhenjun

doi:10.1080/14680629.2017.1308265

Cited by 110 publications

(44 citation statements)

References 31 publications

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“…A novel method based on the beamlet transform technique has been proposed in [ 9 ]; nevertheless, this method was unable to recognize diagonal and alligator cracks. Moreover, intelligent models based on convolution neural networks (CNNs) established in [ 17 , 18 , 63 ] have significant advantages over the models based on image processing algorithms (e.g., Laplacian pyramid and steerable filters); that is, the feature extraction and data classification can be integrated and performed autonomously. These models based on CNNs also attained positive classification accuracy which can be up to 94%; however, they have rarely been performed in multiclass pavement recognition.…”

Section: Resultsmentioning

confidence: 99%

“…Besides the widely used image thresholding methods, the beamlet transform [ 9 ], predesigned image filtering [ 10 ], the Gabor filter [ 11 ], weighted neighborhood segmentation [ 12 ], wavelet-morphology-based detection [ 13 ], fuzzy Hough transform [ 14 ], steerable matched filtering [ 15 ], probabilistic generative model [ 4 ], and optimized minimal path selection [ 16 ] have been investigated by various scholars. Deep learning approaches [ 17 , 18 ] which automate the feature extraction process have also been proposed. Nevertheless, these complex models are also computational intensive and require capable machine to perform the computing tasks.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Hoang

2018

Computational Intelligence and Neuroscience

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To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Hoang

2018

Computational Intelligence and Neuroscience

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“…This is due to the fact that pavement cracks are seen as the most predominant distress type [45] and they are also easier to measure, with the typical requirements being simply to measure the crack's width and length. There are a tremendous number of studies on developing specific neural networks for crack detection and analysis using both 2D and 3D imagery [41,42,[46][47][48][49][50][51][52] and with comparisons made to results from image-based toolboxes for crack detection and analysis such as CrackIT [53]. While the detection and monitoring of cracks are important to road agencies, this represents only one main category of distress.…”

Section: The Use Of Deep Learning In Pavement Engineeringmentioning

confidence: 99%

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

et al. 2020

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Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

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“…Nevertheless, deep learning, as a cascade of multiple layers, has been introduced as a powerful approach for crack identification in recent years 15–17 . Mark the concrete surface image as a crack surface or a complete surface, and use the convolutional neural network (CNN) 18 to train the classification model.…”

Section: Introductionmentioning

confidence: 99%

Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing

Ren

Qiao

et al. 2020

Struct Control Health Monit

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In this study, the bridge concrete structure is taken as the research object, and the real image is used for crack identification. In structural engineering, surface cracks are the main indexes of durability and service performance of structures. Artificial visual inspection is often considered ineffective in terms of cost, safety, evaluation accuracy, and reliability. In this article, a simple, high-classification framework based on ResNeXt with postprocessing (ResNeXt+PP) model is provided to effectively identify concrete cracks. During the training phase of the method, image binarization approach is used to extract the candidate crack regions. It is difficult to automatic identify cracks from images containing actual cracks and noises, especially, shadows, stains, masses, and holesoften occur in concrete surfaces. Thereafter, classification models are constructed based on ResNeXt+PP module. Based on the new concrete surface images including cracks and noncracks, the obtained methods for crack identification are compared quantitatively and qualitatively. Besides, the five complete raw images are used to study the robustness and practicability of the method. The binary transformation process based on a binarization method of adaptive crack width is adopted to identify crack pixels in subimages. Results show that the trained improved ResNeXt+PP can automatically detect cracks and noncracks in the raw image. The obtained results that the method is superior to multiple methods and the application prospect of autonomous concrete structure driver for bridge detection robot are presented.

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Recognition of asphalt pavement crack length using deep convolutional neural networks

Cited by 110 publications

References 31 publications

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing

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