The dataset and code of the paper "Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method".
Authors: Fusheng Yu†, Jiang Li†, Xiaoping Wang, Shaojin Wu, Junjie Zhang, Zhigang Zeng († Equal Contribution)
Affiliation: Huazhong University of Science and Technology (HUST)
@article{yu2024sfchd-scale,
title={Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method},
author={Fusheng Yu and Jiang Li and Xiaoping Wang and Shaojin Wu and Junjie Zhang and Zhigang Zeng},
year={2024},
journal = {},
volume = {},
pages = {},
doi={}
}Comparison between SFCHD and existing open-source datasets for safety helmets
| Dataset | Year | #Category | #Sample | #Instance | Color | Task | Data Source |
|---|---|---|---|---|---|---|---|
| Pictor-v3 | 2020 | 6 | 1,330 | 9,208 | RGB | Detection | Web-mined and Crowd-sourced |
| SHWD | 2019 | 2 | 3,241 | 10,457 | RGB | Detection | Web-mined |
| SFCHD | 2023 | 7 | 12,373 | 50,558 | RGB | Detection | Chemical Plant |
Statistics of instance distribution per category in the SFCHD dataset
| Category | Person | Safety Helmet | Safety Clothing | Other Clothing | Head | Blurred Clothing | Blurred Head | Total |
|---|---|---|---|---|---|---|---|---|
| Training | 13,528 | 11,378 | 11,781 | 626 | 961 | 1,053 | 896 | 40,223 |
| Testing | 3,482 | 2,920 | 3,032 | 154 | 239 | 271 | 238 | 10,336 |
| Total | 17,010 | 14,298 | 14,813 | 780 | 1,200 | 1,324 | 1,134 | 50,559 |
Category distribution of objects with different sizes in the SFCHD dataset
| Category | Total | Large | Medium | Small |
|---|---|---|---|---|
| Safety Helmet | 14,298 | 3,551 | 3,030 | 7,717 |
| Head | 1,200 | 95 | 164 | 941 |
| Blurred Clothing | 1,324 | 281 | 313 | 730 |
| Blurred Head | 1,134 | 15 | 41 | 1,078 |
Comparisons of different methods on the Pictor-v3, SHWD, and SFCHD datasets [mAP(0.50)/mAP(0.50:0.95)]
| Method | Backbone | Pictor-v3 | SHWD | SFCHD (ours) |
|---|---|---|---|---|
| SSD | VGG16 | 85.5 / 48.8 | 80.8 / 57.4 | 72.8 / 41.5 |
| Faster RCNN | ResNet-50 | 90.6 / 53.4 | 84.8 / 63.1 | 76.4 / 50.3 |
| FCOS | ResNet-50 | 89.5 / 52.4 | 85.8 / 63.9 | 76.4 / 49.6 |
| VFNet | ResNet-50 | 91.4 / 55.2 | 85.7 / 63.9 | 76.4 / 51.0 |
| RetinaNet | ResNet-50 | 90.5 / 54.4 | 85.5 / 63.6 | 75.9 / 48.9 |
| TOOD | ResNet-50 | 91.5 / 55.8 | 86.7 / 64.4 | 78.9 / 52.3 |
| YOLOv5 | CSPDarknet53 | 88.2 / 53.6 | 84.0 / 63.9 | 74.1 / 49.6 |
Performance of different categories in the SFCHD dataset
| Method | Person | Safety Helmet | Safety Clothing | Other Clothing | Head | Blurred Clothing | Blurred Head | mAP(0.50:0.95) | mAP(0.50) |
|---|---|---|---|---|---|---|---|---|---|
| SSD | 60.2 | 56.5 | 55.7 | 45.2 | 38.4 | 20.5 | 14.2 | 41.5 | 72.8 |
| Faster RCNN | 71.2 | 64.7 | 64.6 | 54.5 | 49.3 | 27.2 | 20.5 | 50.3 | 76.4 |
| FCOS | 68.4 | 63.4 | 64.6 | 54.0 | 48.0 | 28.9 | 19.6 | 49.6 | 76.4 |
| VFNet | 73.1 | 66.2 | 64.3 | 54.0 | 52.5 | 25.1 | 22.0 | 51.0 | 76.4 |
| RetinaNet | 71.1 | 63.5 | 64.5 | 51.8 | 48.5 | 27.1 | 15.9 | 48.9 | 75.9 |
| TOOD | 72.9 | 66.0 | 65.9 | 56.2 | 52.8 | 29.6 | 22.3 | 52.3 | 78.9 |
| YOLOv5 | 72.7 | 66.4 | 63.7 | 54.9 | 50.7 | 21.2 | 18.9 | 49.6 | 74.1 |
Performance comparisons between our SCALE-YOLO and existing models on the ExDark dataset
| Method | Bicycle | Boat | Bottle | Bus | Car | Cat | Chair | Cup | Dog | Motorbike | People | Table | mAP(0.50) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| YOLOv3 | 79.8 | 75.3 | 78.1 | 92.3 | 83.0 | 68.0 | 69.0 | 79.0 | 78.0 | 77.3 | 81.5 | 55.5 | 76.4 |
| KinD | 80.1 | 77.7 | 77.2 | 93.8 | 83.9 | 66.9 | 68.7 | 77.4 | 79.3 | 75.3 | 80.9 | 53.8 | 76.3 |
| MBLLEN | 82.0 | 77.3 | 76.5 | 91.3 | 84.0 | 67.6 | 69.1 | 77.6 | 80.4 | 75.6 | 81.9 | 58.6 | 76.8 |
| Zero-DCE | 84.1 | 77.6 | 78.3 | 93.1 | 83.7 | 70.3 | 69.8 | 77.6 | 77.4 | 76.3 | 81.0 | 53.6 | 76.9 |
| MAET | 83.1 | 78.5 | 75.6 | 92.9 | 83.1 | 73.4 | 71.3 | 79.0 | 79.8 | 77.2 | 81.1 | 57.0 | 77.7 |
| DENet | 80.4 | 79.7 | 77.9 | 91.2 | 82.7 | 72.8 | 69.9 | 80.1 | 77.2 | 76.7 | 82.0 | 57.2 | 77.3 |
| IAT-YOLO | 79.8 | 76.9 | 78.6 | 92.5 | 83.8 | 73.6 | 72.4 | 78.6 | 79.0 | 79.0 | 81.1 | 57.7 | 77.8 |
| PE-YOLO | 84.7 | 79.2 | 79.3 | 92.5 | 83.9 | 71.5 | 71.7 | 79.7 | 79.7 | 77.3 | 81.8 | 55.3 | 78.0 |
| SCALE-YOLO | 81.3 | 79.3 | 78.2 | 93.9 | 84.2 | 75.5 | 74.9 | 82.3 | 81.0 | 77.5 | 82.5 | 57.3 | 79.0 |
Performance improvements of the SCALE module on the ExDark dataset
| Method | Bicycle | Boat | Bottle | Bus | Car | Cat | Chair | Cup | Dog | Motorbike | People | Table | mAP(0.50) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| FCOS | 75.5 | 64.4 | 68.0 | 86.8 | 78.5 | 69.3 | 55.4 | 71.7 | 70.0 | 64.8 | 72.3 | 46.7 | 68.6 |
| FCOS+SCALE | 75.1 | 66.6 | 73.5 | 89.9 | 78.9 | 67.0 | 57.2 | 72.8 | 74.2 | 67.3 | 72.0 | 45.9 | 70.0 |
| VFNet | 77.4 | 70.5 | 76.6 | 90.6 | 81.8 | 67.1 | 59.4 | 71.8 | 72.6 | 70.6 | 77.7 | 53.3 | 72.5 |
| VFNet+SCALE | 79.4 | 70.2 | 76.5 | 89.7 | 81.7 | 71.9 | 60.9 | 71.5 | 75.0 | 71.2 | 77.4 | 55.5 | 73.4 |
| TOOD | 77.0 | 69.2 | 72.2 | 90.0 | 80.0 | 72.6 | 63.0 | 71.8 | 71.0 | 71.9 | 76.2 | 52.2 | 72.3 |
| TOOD+SCALE | 77.9 | 70.0 | 78.3 | 90.0 | 80.7 | 69.1 | 62.0 | 72.4 | 73.7 | 69.2 | 78.1 | 54.2 | 73.0 |
Ablation analysis for different pathways in our SCALE module
| Method | SAP | CAP | mAP(0.50) |
|---|---|---|---|
| YOLOv3 | -- | -- | 76.4 |
| SCALE-YOLO (Ours) | ✓ | ✗ | 77.3 |
| SCALE-YOLO (Ours) | ✗ | ✓ | 77.8 |
| SCALE-YOLO (Ours) | ✓ | ✓ | 79.0 |
Performance improvements of the SCALE module on the SFCHD dataset
| Method | Person | Safety Helmet | Safety Clothing | Other Clothing | Head | Blurred Clothing | Blurred Head | mAP(0.50:0.95) | mAP(0.50) |
|---|---|---|---|---|---|---|---|---|---|
| FCOS | 68.4 | 63.4 | 64.6 | 54.0 | 48.0 | 28.9 | 19.6 | 49.6 | 76.4 |
| FCOS+SCALE | 68.5 | 63.8 | 64.7 | 53.0 | 47.6 | 28.4 | 20.5 | 49.5 | 76.3 |
| VFNet | 73.1 | 66.2 | 64.3 | 54.0 | 52.5 | 25.1 | 22.0 | 51.0 | 76.4 |
| VFNet+SCALE | 73.2 | 66.5 | 64.4 | 53.9 | 52.1 | 25.8 | 23.7 | 51.4 | 76.6 |
| TOOD | 72.9 | 66.0 | 65.9 | 56.2 | 52.8 | 29.6 | 22.3 | 52.3 | 78.9 |
| TOOD+SCALE | 72.9 | 66.2 | 66.2 | 56.2 | 51.6 | 29.6 | 23.5 | 52.4 | 79.3 |
| YOLOv8 | 73.6 | 64.9 | 67.5 | 58.5 | 45.5 | 32.3 | 23.9 | 52.2 | 77.9 |
| YOLOv8+SCALE | 74.4 | 66.1 | 68.8 | 58.4 | 47.5 | 33.2 | 25.2 | 53.3 | 78.6 |
.
├── annotations
├── classes.txt
├── directory.md
├── images
├── labels
├── labels.cache
├── new_split_yolo
├── sd_train
├── train
├── Vision
└── yolo
8 directories, 3 files
Download the dataset from [链接：https://pan.baidu.com/s/1k2pWg8r-G3KSI2Q3Tdt6kg 提取码：v4ao], unzip and copy the files from images into dataset_SFCHD/images. Also, unzip labels.zip and yolo.zip.
Google Drive: https://drive.google.com/file/d/1-2z7r3J4sZdLvVt5mllvSEwAFO49Y-zj/view?usp=sharing