Fully Convolutional Networks for Automatic Pavement Crack Segmentation

Escalona, Uriel; Arce, Fernando; Zamora, Erik; Azuela, Juan Humberto Sossa

doi:10.13053/cys-23-2-3047

Cited by 30 publications

(25 citation statements)

References 17 publications

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“…The work of [57] employed residue connections inside each encoder and decoder block and attention gating block before the decoder to retain only spatially relevant features of the feature map in the shortcut connection. Fully convolutional network is also often used for segmentation purpose, such as [58], [59].…”

Section: B: Crack Detection Based On Pixel Segmentationmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

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Section: B: Crack Detection Based On Pixel Segmentationmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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“…With binary_crossentropy, we can interpret the probability of crack in each pixel from 0 to 1 as the probability of crack on different positions. [48][49][50] Through this approach, the crack prediction problem become a multiclassification problem.…”

Section: Machine-learning Modelmentioning

confidence: 99%

Using Deep Learning to Predict Fracture Patterns in Crystalline Solids

Hsu

Buehler

2020

Matter

117

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“…is network further improves the accuracy of crack detection. Literature [21] compared three U-Net algorithms of different depths for automatic pavement crack detection systems. e objective is to verify whether a model architecture with greater depth necessarily results in better detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

Zheng

Wang

et al. 2022

Journal of Advanced Transportation

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This work presents a new method for sleeper crack identification based on cascade convolutional neural network (CNN) to address the problem of low efficiency and poor accuracy in the traditional detection method of sleeper crack identification. The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). The improved YOLOv3 network is used to identify and locate cracks on sleepers and segment them after the sleeper on the ballast bed is extracted by using the gray projection method. The sleeper is inputted into CEDNet for crack feature extraction to predict the coarse crack saliency map. The prediction graph is inputted into CRRNet to improve its edge information and local region to achieve optimization. The accuracy of the crack identification model is improved by using a mixed loss function of binary cross-entropy (BCE), structural similarity index measure (SSIM), and intersection over union (IOU). Results show that this method can accurately detect the sleeper crack image. During object detection, the proposed method is compared with YOLOv3 in terms of directly locating sleeper cracks. It has an accuracy of 96.3%, a recall rate of 91.2%, a mean average precision (mAP) of 91.5%, and frames per second (FPS) of 76.6/s. In the crack extraction part, the F-weighted is 0.831, mean absolute error (MAE) is 0.0157, and area under the curve (AUC) is 0.9453. The proposed method has better recognition, higher efficiency, and robustness compared with the other network models.

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Fully Convolutional Networks for Automatic Pavement Crack Segmentation

Cited by 30 publications

References 17 publications

Review of Pavement Defect Detection Methods

Review of Pavement Defect Detection Methods

Using Deep Learning to Predict Fracture Patterns in Crystalline Solids

Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

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