A Convolutional Neural Network for Pavement Surface Crack Segmentation Using Residual Connections and Attention Gating

König, Jacob; Jenkins, M.; Barrie, Peter; Mannion, Mike; Morison, Gordon

doi:10.1109/icip.2019.8803060

Cited by 66 publications

(41 citation statements)

References 15 publications

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“…And in [56], the propose network structure used 4 convolution layers and max poolings as the encoder to extract features and 4 subsequent modules as the decoder. 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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“…For the proposed U-HDN, the transitional areas between non-crack and crack pixels were considered before computing

, and

. Considering the subjective manual labels for ground truth, the transitional areas (2 pixels distance) between crack and non-crack pixels are accepted in these papers [ 41 , 56 , 57 , 79 , 80 ]. Therefore, 2 pixels of distance is accepted in this project.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Fan

Chen

et al. 2020

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Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

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“…For the ensemble networks, when we calculate the metric for TP, FP, and FN, we consider the transitional areas between non-crack and crack pixels. Therefore, a two-pixels distance between the prediction image and the ground truth is accepted in [20,29,45,46]. In this project, we accepted a two-pixel distance.…”

Section: Training and Testingmentioning

confidence: 99%

Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

et al. 2020

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Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-theart methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

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A Convolutional Neural Network for Pavement Surface Crack Segmentation Using Residual Connections and Attention Gating

Cited by 66 publications

References 15 publications

Review of Pavement Defect Detection Methods

Review of Pavement Defect Detection Methods

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

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