CrackSeU

This repository is the official implementation of the Crack Segmentation U-shape (CrackSeU) Network.

🔥 Break News:

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 )

🛴 Getting Started

1. Requirement

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.

2. Installation

git clone https://github.com/hzlbbfrog/CrackSeU
cd CrackSeU
pip install -r Requirements.txt

Or, you can directly "Download ZIP".

3. Build your own dataset

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

4. Training

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

5. Test

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

🚀 The network architecture of CrackSeU:

🚀 Illustration of the proposed FFM:

🎖️ Results

Performance comparison of different methods on Concretecrack

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.

Quantitative evaluation of different models on Deepcrack

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

💘 Citing CrackSeU

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}
}

👅 Acknowledgements

SEU is also the abbreviation of Southesast Univertisy.
The name of our framework ( CrackSeU) is also dedicated to the 120th anniversary of Southeast University.