Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V

Fei, Yue; Wang, Kelvin C. P.; Zhang, Allen A.; Chen, Cheng; Li, Joshua; Liu, Yang; Yang, Guangwei; Li, Baoxian

doi:10.1109/tits.2019.2891167

Cited by 236 publications

(91 citation statements)

References 32 publications

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“…Zou et al (2018) developed an automated crack detection method DeepCrack, which uses an end-to-end trainable deep CNN to learn high-level characteristics for crack presentation. Fei et al (2019) developed a pavement crack detection method CrackNet-V, which conducts the supervised pixel-level learning by all-layer constant spatial size. D. Zhang et al (2018) proposed a sparse processing automated pavement distress detection algorithm based on laser scanning 3D data.…”

Section: Introductionmentioning

confidence: 99%

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Liu

Yang

Lau

et al. 2020

Computer aided Civil Eng

270

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Cracking is a common pavement distress that would cause further severe problems if not repaired timely, which means that it is important to accurately extract the information of pavement cracks through detection and segmentation. Automated pavement crack detection and segmentation using deep learning are more efficient and accurate than conventional methods, which could be further improved. While many existing studies have utilized deep learning in pavement crack segmentation, which segments cracks from non-crack regions, few studies have taken the exact pavement crack detection into account, which identifies cracks in the images from other objects. A two-step pavement crack detection and segmentation method based on convolutional neural network was proposed in this paper. An automated pavement crack detection algorithm was developed using the modified You Only Look Once 3rd version in the first step. The proposed crack segmentation method in the second step was based on the modified U-Net, whose encoder was replaced with a pre-trained ResNet-34 and the upsample part was added with spatial and channel squeeze and excitation (SCSE) modules. Proposed method combines pavement crack detection and segmentation together, so that the detected cracks from the first step are segmented in the second step to improve the accuracy. A dataset of pavement crack images in different circumstances were also built for the study. The F1 score of proposed crack detection and segmentation methods are 90.58% and 95.75%, respectively, which are higher than other state-of-the-art methods. Compared with existing one-step pavement crack detection or segmentation methods, proposed two-step method showed advantages of accuracy.

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Section: Introductionmentioning

confidence: 99%

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Liu

Yang

Lau

et al. 2020

Computer aided Civil Eng

270

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“…However, these small blocks do not provide sufficient contextual information, which easily causes the detected cracks to be fractured. Fei et al [18] used an FCN for crack detection, and the method achieved high accuracy and speed. Liu et al [20] used dilated convolution and the squeeze-and-excitation learning operation for crack detection, which was found to further improve the detection accuracy and speed.…”

Section: Deep Learningmentioning

confidence: 99%

“…Moreover, according to the detection results, the attributes of pavement cracks, such as the width and length, cannot be obtained, which is very unfavorable to the evaluation of the pavement surface. Some researchers have used semantic segmentation to detect cracks; for example, in some studies [17], [18] a fully convolutional network (FCN) [19] has been applied to pixel-level crack detection. In one method [20], dilated convolution [21]- [24] and the squeeze-and-excitation learning operation [25] are used for crack detection, which has been found to improve the detection accuracy and speed.…”

Section: Introductionmentioning

confidence: 99%

APLCNet: Automatic Pixel-Level Crack Detection Network Based on Instance Segmentation

Zhang

Chen

Wang³

et al. 2020

IEEE Access

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The accurate and automatic detection of pavement cracks is essential for pavement maintenance. However, automatic crack detection remains a challenging problem due to the inconspicuous visual features of cracks in complex pavement backgrounds, the complicated shapes and structures of cracks, and the influences of weather changes and noise. In recent years, with the development of artificial intelligence technology, crack detection methods based on classification and semantic segmentation have laid a good foundation for the automation of pavement crack detection. However, there remain shortcomings in the comprehensive acquisition of pavement crack attribute information and detection accuracy. To solve these problems, this paper proposes an instance segmentation network for pavement crack detection. The network can simultaneously obtain the crack category, position, and mask, and can realize end-to-end pixel-level crack detection. A semantic segmentation branch is first added to Mask R-CNN. This branch can extract the bottom-level detail information of the cracks and ultimately improves the accuracy of crack mask prediction. An adaptive feature fusion module is then designed. During feature fusion, this module highlights the attribute information and location information of cracks according to the channel attention mechanism and the spatial attention mechanism. Finally, these two modules are integrated to form an automatic pixel-level crack detection network, namely APLCNet. Without any embellishment, APLCNet achieves a precision of 92.21%, a recall of 94.89%, and an F1-score of 93.53% on the challenging public CFD dataset, thereby outperforming CrackForest and MFCD for pixel-wise crack detection. Moreover, APLCNet achieves a 16.5% mask AP on the self-captured GDPH dataset, thereby surpassing Mask R-CNN and PANet.

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“…However, the detailed division of the crack could not be completed. In [26] and [27], 3D crack detection networks based on DCNN are proposed for automated pixel-level crack detection on 3D asphalt pavement. In [28], an e ective detection model for concrete cracks is proposed through two modules of multi-view image feature detection and multitask crack detection.…”

Section: Introductionmentioning

confidence: 99%

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Song

Jia

Zhu

et al. 2020

Journal of Advanced Transportation

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Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

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Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V

Cited by 236 publications

References 32 publications

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

APLCNet: Automatic Pixel-Level Crack Detection Network Based on Instance Segmentation

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

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