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Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
| Content Provider | MDPI |
|---|---|
| Author | Hayat, Ahatsham Morgado-Dias, Fernando |
| Copyright Year | 2022 |
| Description | Worker safety at construction sites is a growing concern for many construction industries. Wearing safety helmets can reduce injuries to workers at construction sites, but due to various reasons, safety helmets are not always worn properly. Hence, a computer vision-based automatic safety helmet detection system is extremely important. Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites. This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site. YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection. A benchmark dataset containing 5000 images of hard hats was used in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively. The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of 92.44%, thereby showing excellent results in detecting safety helmets even in low-light conditions. |
| Starting Page | 8268 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12168268 |
| Journal | Applied Sciences |
| Issue Number | 16 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-18 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Computer Vision Safety Helmet Detection You Only Look Once (yolo) Deep Learning |
| Content Type | Text |
| Resource Type | Article |
