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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
git clone https://github.com/huangyebiaoke/steel-pipe-weld-defect-detection
Go to the project directory
cd steel-pipe-weld-defect-detection
Install dependencies
pip install -r requirements.txt
Download dataset from Releases and unzip the file to the current directory
wget https://github.com/huangyebiaoke/steel-pipe-weld-defect-detection/releases/download/1.0/steel-tube-dataset-all.zip
unzip steel-tube-dataset-all.zip
Start training model
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 | 气孔 | 咬边 | 断弧 | 裂缝 | 夹珠 | 焊瘤 | 夹渣 | 未融合 |
| 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}
}