IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems
Deep learning has recently been extensively used for crack detection in structural health monitoring settings. However, detecting cracks in levee systems have yet to receive considerable critical attention. Thus, this study presents a novel encoder-decoder-based fully convolutional neural network to...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10035954/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846128528001597440 |
|---|---|
| author | Manisha Panta Md Tamjidul Hoque Mahdi Abdelguerfi Maik C. Flanagin |
| author_facet | Manisha Panta Md Tamjidul Hoque Mahdi Abdelguerfi Maik C. Flanagin |
| author_sort | Manisha Panta |
| collection | DOAJ |
| description | Deep learning has recently been extensively used for crack detection in structural health monitoring settings. However, detecting cracks in levee systems have yet to receive considerable critical attention. Thus, this study presents a novel encoder-decoder-based fully convolutional neural network to detect cracks from levee images at a pixel level automatically. We propose that the feature learning be strengthened using the decoder and bottleneck feature maps by concatenating them back to the encoder blocks. The addition reinforcement in the U-Net-like architecture results in a loop-like structure to exploit all the feature maps from encoders, bottlenecks, and decoders. The proposed architecture, Iterative Loop U-Net (IterLUNet), outperforms the state-of-the-art architectures on the image dataset of the levee system, achieving an increment of Intersection over Union (IoU) by 10.32% on average for a 10-Fold Cross-Validation (FCV) compared to the baseline U-Net model and 11.00%, 7.65%, and 7.43% with a range of latest models MultiResUnet, Attention U-Net, and Unet++ respectively. In addition, IterLUNet has at least 63% fewer parameters to be trained than the baseline model, thus, allowing less space consumption for pixel-wise crack detection in AI-based inspection of levee systems. |
| format | Article |
| id | doaj-art-3d9d0e1f4d0e4dbcbb395014907846b8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3d9d0e1f4d0e4dbcbb395014907846b82024-12-11T00:04:04ZengIEEEIEEE Access2169-35362023-01-0111122491226210.1109/ACCESS.2023.324187710035954IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee SystemsManisha Panta0Md Tamjidul Hoque1https://orcid.org/0000-0002-0110-2194Mahdi Abdelguerfi2Maik C. Flanagin3Canizaro Livingston Gulf States Center for Environmental Informatics, The University of New Orleans, New Orleans, LA, USACanizaro Livingston Gulf States Center for Environmental Informatics, The University of New Orleans, New Orleans, LA, USACanizaro Livingston Gulf States Center for Environmental Informatics, The University of New Orleans, New Orleans, LA, USAU.S. Army Corps of Engineers, New Orleans, LA, USADeep learning has recently been extensively used for crack detection in structural health monitoring settings. However, detecting cracks in levee systems have yet to receive considerable critical attention. Thus, this study presents a novel encoder-decoder-based fully convolutional neural network to detect cracks from levee images at a pixel level automatically. We propose that the feature learning be strengthened using the decoder and bottleneck feature maps by concatenating them back to the encoder blocks. The addition reinforcement in the U-Net-like architecture results in a loop-like structure to exploit all the feature maps from encoders, bottlenecks, and decoders. The proposed architecture, Iterative Loop U-Net (IterLUNet), outperforms the state-of-the-art architectures on the image dataset of the levee system, achieving an increment of Intersection over Union (IoU) by 10.32% on average for a 10-Fold Cross-Validation (FCV) compared to the baseline U-Net model and 11.00%, 7.65%, and 7.43% with a range of latest models MultiResUnet, Attention U-Net, and Unet++ respectively. In addition, IterLUNet has at least 63% fewer parameters to be trained than the baseline model, thus, allowing less space consumption for pixel-wise crack detection in AI-based inspection of levee systems.https://ieeexplore.ieee.org/document/10035954/Crack detectiondeep learningfloodwallsimage segmentationlevees |
| spellingShingle | Manisha Panta Md Tamjidul Hoque Mahdi Abdelguerfi Maik C. Flanagin IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems IEEE Access Crack detection deep learning floodwalls image segmentation levees |
| title | IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems |
| title_full | IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems |
| title_fullStr | IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems |
| title_full_unstemmed | IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems |
| title_short | IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems |
| title_sort | iterlunet deep learning architecture for pixel wise crack detection in levee systems |
| topic | Crack detection deep learning floodwalls image segmentation levees |
| url | https://ieeexplore.ieee.org/document/10035954/ |
| work_keys_str_mv | AT manishapanta iterlunetdeeplearningarchitectureforpixelwisecrackdetectioninleveesystems AT mdtamjidulhoque iterlunetdeeplearningarchitectureforpixelwisecrackdetectioninleveesystems AT mahdiabdelguerfi iterlunetdeeplearningarchitectureforpixelwisecrackdetectioninleveesystems AT maikcflanagin iterlunetdeeplearningarchitectureforpixelwisecrackdetectioninleveesystems |