Life cycle environmental benefits of pavement surface maintenance

Pellecuer, Luc; Assaf, Gabriel J.; St-Jacques, Michèle

doi:10.1139/cjce-2013-0508

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…Water can infiltrate into the road structure through the cracks to cause water damage and reduce the stability of pavement layers, which will cause further distresses like potholes and pavement structure failure (Jassal, 1998). The service life of asphalt pavement can be prolonged by a timely maintenance and repair for early‐stage crack (Pellecuer, Assaf, & St‐Jacques, 2014). The current practice of pavement crack detection and repair is mainly manually based, where the cracks are mainly identified by human and filled with crack sealants by hand.…”

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

271

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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

271

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show abstract

“…This provides flexibility to the network, allowing the network to choose between the channel refined or original MLF as needed. 49 The final channel refined MLF can be summarized in equation (4).…”

Section: Self-guided Attention Refinement Modulementioning

confidence: 99%

“…3 Therefore, agencies must conduct inspections thoroughly and consistently as timely repairs can prevent the worsening of road conditions. 4 Traditionally, this inspection is conducted on-site by trained operators, but this process is labour intensive, erroneous and tedious. 5 Today, the industry has developed sophisticated camera systems to capture pavement cracks.…”

Section: Introductionmentioning

confidence: 99%

Feature pyramid network with self-guided attention refinement module for crack segmentation

Ong

Lau

Ismadi

et al. 2022

Structural Health Monitoring

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Automated pavement crack segmentation is challenging due to the random shape of cracks, complex background textures and the presence of miscellaneous objects. In this paper, we implemented a Self-Guided Attention Refinement module and incorporated it on top of a Feature Pyramid Network (FPN) to model long-range contextual information. The module uses multi-scale features integrated from different layers in the FPN to refine the features at each layer of the FPN using a self-attention mechanism. The module enables the earlier layers and deeper layers of FPN to suppress noise and increase the crack details, respectively. The proposed network obtains an F1 score of 79.43% and 96.19% on the Crack500 and CFD datasets, respectively. Furthermore, the network also generalizes better than other state-of-the-art methods when tested on uncropped Crack500 and field images using the weights trained on CFD. In addition, ablation tests using the Crack500 dataset are conducted. The Self-Guided Attention Refinement module increases the average F1 score and recall by 0.6% and 0.8% while roughly maintaining the average precision. From the ablation test, the inclusion of the Self-Guided Attention Refinement module allows the network to segment finer and more continuous cracks. Then, the module is incorporated on PANet, DeepLab v3+ and U-Net to verify the improvements made to FPN. The results show that the module improves the F1 score, precision and recall compared to the absence of it. Moreover, the Self-Guided Attention Refinement Module is compared with the Self-Adaptive Sparse Transform Module (SASTM). The results show that the Self-Guided Attention Refinement Module provides a more consistent improvement.

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“…e transportation agency officials regard pavement data collection as a regular work to grasp the evolution of road conditions and make opportune work to stop the deterioration of the distress. Efficient pavement condition inspections and reasonable repair strategies can lead to a significant reduction in life-cycle pavement maintenance cost [2].…”

Section: Introductionmentioning

confidence: 99%

The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

Jiang

Yuan

Lang

et al. 2022

Journal of Advanced Transportation

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Crack is a common concrete pavement distress that will deteriorate into severe problems without timely repair, which means the automated detection of pavement crack is essential for pavement maintenance. However, automatic crack detection and segmentation remain challenging due to the complex pavement condition. Recent research on pavement crack detection based on deep learning has laid a good foundation for automated crack segmentation, but there can still be improvements. This paper proposes an automatic concrete pavement crack segmentation framework with enhanced graph network branch. First, the nodes of the graph and nodes’ attributions are generated based on the image dividing. The edges of the graph are determined based on Gaussian distribution. Then, the graph from the image is input into the graph branch. The graph feature map of the graph branch output is fused with the image feature map of the encoder and then enters the decoder to recover the image resolution to obtain the crack segmentation result. Finally, the method is tested on a self-built 3D concrete pavement crack dataset. The proposed method achieves the highest F1 and IoU (Intersection over Union) in the comparison experiments. And the graph branch addition improves 0.08 on F1 and 0.06 on IoU compared with U-Net.

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Life cycle environmental benefits of pavement surface maintenance

Cited by 14 publications

References 26 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

Feature pyramid network with self-guided attention refinement module for crack segmentation

The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

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