CIB-SE-YOLOv8: Optimized YOLOv8 for Real-Time Safety Equipment Detection on Construction Sites
This provides a more effective solution for safety compliance on construction sites, but it is incremental as it builds on existing YOLO methods.
The study tackled real-time helmet detection on construction sites using a computer vision approach, resulting in an optimized YOLOv8 model that enhanced detection accuracy and efficiency.
Ensuring safety on construction sites is critical, with helmets playing a key role in reducing injuries. Traditional safety checks are labor-intensive and often insufficient. This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset. Our proposed CIB-SE-YOLOv8 model incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency. This model offers a more effective solution for promoting safety compliance on construction sites.