SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion
⭐ This code has been completely released ⭐
⭐ our article ⭐
If our code is helpful to you, please cite:
@article{li2024scfnet,
title={SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion},
author={Li, Hongli and Yi, Zhiqi and Mei, Liye and Duan, Jia and Sun, Kaimin and Li, Mengcheng and Yang, Wei and Wang, Ying},
journal={Processes},
volume={12},
number={5},
pages={931},
year={2024},
publisher={MDPI}
}
## Requirements
```python
pip install -r requirements.txt
- The download link for the NEU-DET data set is here.
- The download link for the GC10-DET data set is here.
SCFNet
├── NEU-DET
│ ├── images
│ │ ├── train
│ │ │ ├── crazing_1.jpg
│ │ │ ├── crazing_2.jpg
│ │ │ ├── .....
│ │ ├── val
│ │ ├── test
│ ├── labels
│ │ ├── train
│ │ │ ├── crazing_1.txt
│ │ │ ├── crazing_2.txt
│ │ │ ├── .....
│ │ ├── val
│ │ ├── test
- After downloading the data set, modify the paths in path, train, val and test in the data.yaml file.
python train.py
python val.py
Methods | P | R | mAP50 | mAP50:95 |
GFLOPs |
Params/M |
---|---|---|---|---|---|---|
Faster R-CNN | 0.615 | 0.865 | 0.76 | 0.377 | 135 | 41.75 |
CenterNet | 0.712 | 0.749 | 0.764 | 0.412 | 123 | 32.12 |
YOLOv5n-7.0 | 0.694 | 0.694 | 0.746 | 0.422 | 4.2 | 1.77 |
YOLOv5s-7.0 | 0.745 | 0.719 | 0.761 | 0.429 | 15.8 | 7.03 |
YOLOv7-tiny | 0.645 | 0.775 | 0.753 | 0.399 | 13.1 | 6.02 |
YOLOv8s | 0.768 | 0.726 | 0.795 | 0.467 | 28.4 | 11.13 |
YOLOX-tiny | 0.746 | 0.768 | 0.76 | 0.357 | 7.58 | 5.03 |
MRF-YOLO | 0.761 | 0.707 | 0.768 | - | 29.7 | 14.9 |
YOLOv5s-FCC | - | - | 0.795 | - | - | 13.35 |
WFRE-YOLOv8s | 0.759 | 0.736 | 0.794 | 0.425 | 32.6 | 13.78 |
CG-Net | 0.734 | 0.687 | 0.759 | 0.399 | 6.5 | 2.3 |
ACD-YOLO | - | - | 0.793 | - | 21.3 | - |
YOLOv5-ESS | - | 0.764 | 0.788 | - | - | 7.07 |
PMSA-DyTr | - | - | 0.812 | - | - | - |
MED-YOLO | - | - | 0.731 | 0.376 | 18 | 9.54 |
MAR-YOLO | - | - | 0.785 | - | 20.1 | - |
SCFNet | 0.786 | 0.715 | 0.812 | 0.469 | 5.9 | 2 |
- Bold indicates first or second best performance.
2024.4.25 open the val.py
2024.5.16 update train.py
2024.5.16 update ScConv module.
This code is built on ultralytics (PyTorch). We thank the authors for sharing the codes.
If you have any questions, please contact me by email (lazyshark2001@gmail.com).