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Encoder–decoder with pyramid region attention for pixel‐level pavement crack recognition
Computer-Aided Civil and Infrastructure Engineering
Swansea University Author:
Yue Hou
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DOI (Published version): 10.1111/mice.13128
Abstract
Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manua...
| Published in: | Computer-Aided Civil and Infrastructure Engineering |
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| ISSN: | 1093-9687 1467-8667 |
| Published: |
Wiley
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65225 |
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| Abstract: |
Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non-local (NL) block, which has the ability to efficiently capture long-range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi-scale context integration and long-range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U-Net, an effective crack segmentation model called CrackResU-Net was developed. The test results on the existing CrackForest dataset showed that CrackResU-Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state-of-the-art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high-precision recognition of pavement cracks for engineering purposes. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
Hunan Expressway Group Co. Ltd and the Hunan Department of Transportation (No. 202152) in China. The authors also appreciate the funding support from the National Natural Science Foundation of China (No. 51778038), the China National Key R&D Program (2021YFB2600600, 2021YFB2600604, 2018YFB1600202), National Natural Science Foundation of China (grant number: 52108403), |