This repository is the official implementation of the Crack Segmentation U-shape (CrackSeU) Network.
Our paper is finally accepted by Automation in Construction after a year of review. I have to say it has been a long and tough journey. 😭.
The paper is available:
Online monitoring of crack dynamic development using attention-based deep networks, Automation in Construction, 154 (2023) 105022, by Wang chen*, Zhili He*, and Jian Zhang#. ( *: Co-first authors, #: Corresponding Author )
Recommended versions are
* python = 3.5
* pytorch = 1.12.1
* CUDA 11.6.2 and CUDNN 8.6.0
Other requirements can be found in the Requirements.txt.
git clone https://github.com/hzlbbfrog/CrackSeU
cd CrackSeU
pip install -r Requirements.txt
Or, you can directly "Download ZIP".
You can refer to the following file tree to organize your own data.
Your project
│ README.md
│ ...
│ CrackSeU_main.py
│
└───Dataset
|
└───Your dataset name
|
└───Train
└───images
└───masks
└───Test
└───images
└───masks
│
└───...Other directories
- Include CrackSeU-B with LN_VT.
- Include CrackSeU-B with BN.
- Include CrackSeU-B with LN_Pytorch.
- Include CrackSeU-B with LN_He.
To train the CrackSeU-B with LN_VT, simply run:
python CrackSeU_main.py --action=train --arch=CrackSeU_B_LN_VT --epoch=50 --batch_size=2 --lr=1e-4
To test the CrackSeU-B with LN_VT, simply run:
python CrackSeU_main.py --action=test --arch=CrackSeU_B_LN_VT --test_epoch=50
Method | m IoU (%) | mi IoU (%) | mi Dice (%) | #Param. (M) | MACs (G) |
---|---|---|---|---|---|
U-Net | 81.04 | 75.35 | 81.20 | 7.77 | 55.01 |
U-Net (large) | 82.65 | 76.18 | 81.40 | 31.04 | 219.01 |
U-Net++ | 79.51 | 74.02 | 80.14 | 9.16 | 138.63 |
U-Net++ (large) | 80.33 | 74.50 | 81.03 | 36.63 | 552.67 |
Attention U-Net | 82.87 | 75.85 | 81.17 | 34.88 | 266.54 |
CE-Net | 81.28 | 75.25 | 81.09 | 29.00 | 35.60 |
CrackSeU-B | 85.74 | 81.32 | 88.55 | 3.19 | 11.22 |
CrackSeU-M | 85.85 | 81.53 | 88.66 | 3.58 | 15.04 |
CrackSeU-L | 86.39 | 82.09 | 89.11 | 4.62 | 28.22 |
It is worth noting that the number of parameters of CrackSeU-L is 4.62M.
In the original paper, we mistakenly considered the parameters of the SOB so that the data is 4.70M and a little higher than the true #Param. (4.62M).
We are really sorry if this makes you confused.
Method | m IoU (%) | mi IoU (%) | mi Dice (%) | F1 score | #Param. (M) | MACs (G) |
---|---|---|---|---|---|---|
U-Net | 69.41 | 68.17 | 75.07 | 78.16 | 7.77 | 43.84 |
U-Net (large) | 69.61 | 68.40 | 75.64 | 78.41 | 31.04 | 174.53 |
U-Net++ | 70.19 | 67.92 | 74.91 | 78.20 | 9.16 | 110.47 |
Attention U-Net | 71.48 | 69.19 | 75.11 | 79.16 | 34.88 | 212.40 |
CE-Net | 69.24 | 68.80 | 76.10 | 79.30 | 29.00 | 28.37 |
DeepLabv3+ (MobileNetv2) | 69.70 | 69.18 | 74.23 | 78.34 | 5.81 | 23.25 |
DeepLabv3+ (ResNet-101) | 70.15 | 67.52 | 73.82 | 78.38 | 59.34 | 70.80 |
CrackSeU-B | 73.80 | 81.32 | 81.40 | 81.82 | 3.19 | 8.94 |
You are very welcome to cite our paper! The BibTeX entry is as follows:
@article{CrackSeU,
title = {Online monitoring of crack dynamic development using attention-based deep networks},
journal = {Automation in Construction},
volume = {154},
pages = {105022},
year = {2023},
doi = {https://doi.org/10.1016/j.autcon.2023.105022},
url = {https://www.sciencedirect.com/science/article/pii/S0926580523002820},
author = {Wang Chen and Zhili He and Jian Zhang},
keywords = {Crack identification, Online monitoring method, Deep learning}
}
SEU is also the abbreviation of Southesast Univertisy.
The name of our framework ( CrackSeU) is also dedicated to the 120th anniversary of Southeast University.