steel-pipe-weld-defect-detection
Deep Learning Based Steel Pipe Weld Defect Detection
https://github.com/huangyebiaoke/steel-pipe-weld-defect-detection
Science Score: 49.0%
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○CITATION.cff file
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✓.zenodo.json file
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✓DOI references
Found 8 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○Scientific vocabulary similarity
Low similarity (11.5%) to scientific vocabulary
Keywords
Repository
Deep Learning Based Steel Pipe Weld Defect Detection
Basic Info
Statistics
- Stars: 83
- Watchers: 2
- Forks: 21
- Open Issues: 6
- Releases: 1
Topics
Metadata Files
README.md
Steel Pipe Weld Defect Detection
This repository contains the codes & dataset for the paper: Dingming Yang, Yanrong Cui, Zeyu Yu & Hongqiang Yuan. (2021). Deep Learning Based Steel Pipe Weld Defect Detection. [paper] [arxiv] [code]
Run Locally
Clone the project
bash
git clone https://github.com/huangyebiaoke/steel-pipe-weld-defect-detection
Go to the project directory
bash
cd steel-pipe-weld-defect-detection
Install dependencies
bash
pip install -r requirements.txt
Download dataset from Releases and unzip the file to the current directory
bash
wget https://github.com/huangyebiaoke/steel-pipe-weld-defect-detection/releases/download/1.0/steel-tube-dataset-all.zip
bash
unzip steel-tube-dataset-all.zip
Start training model
bash
py ./yolov5/train.py
Dataset
You can get the dataset from Releases which with YOLO and PASCAL VOC 2007 Format in the zip file.
Sample distribution
| EN | air-hole | bite-edge | broken-arc | crack | hollow-bead | overlap | slag-inclusion | unfused | | ------ | -------- | --------- | ---------- | ----- | ----------- | ------- | -------------- | ------- | | ZH | | | | | | | | | | Label | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | | Number | 5191 | 35 | 458 | 119 | 229 | 223 | 120 | 408 |
Dataset preview
Dataset analysis
Citation
If you use the code or dataset provided in this repository, please cite this work as follows:
@article{doi:10.1080/08839514.2021.1975391,
author = {Dingming Yang and Yanrong Cui and Zeyu Yu and Hongqiang Yuan},
title = {Deep Learning Based Steel Pipe Weld Defect Detection},
journal = {Applied Artificial Intelligence},
volume = {0},
number = {0},
pages = {1-13},
year = {2021},
publisher = {Taylor & Francis},
doi = {10.1080/08839514.2021.1975391},
URL = {https://doi.org/10.1080/08839514.2021.1975391},
eprint = {https://doi.org/10.1080/08839514.2021.1975391}
}
Related works
Acknowledgements
License
Owner
- Name: Dylan Yang
- Login: huangyebiaoke
- Kind: user
- Website: https://blog.madeai.cn
- Twitter: 1536711290M
- Repositories: 2
- Profile: https://github.com/huangyebiaoke
GitHub Events
Total
- Watch event: 18
- Fork event: 6
Last Year
- Watch event: 18
- Fork event: 6
Dependencies
- Cython *
- Pillow *
- PyYAML >=5.3
- matplotlib >=3.2.2
- numpy >=1.18.5
- opencv-python >=4.1.2
- pandas *
- pycocotools >=2.0
- scipy >=1.4.1
- seaborn >=0.11.0
- tensorboard >=2.2
- tensorflow-gpu ==2.2.0
- thop *
- torch >=1.7.0
- torchvision >=0.8.1
- tqdm >=4.41.0