This project showcases a custom YOLOv5 model designed to detect safety violations in real-time CCTV footage. It effectively identifies workers lacking helmets, vests, or goggles, serving as a vital tool to enhance workplace safety and prevent accidents.
Key Features:
Benefits:
Deployment: The model can be seamlessly integrated into existing CCTV systems or deployed on edge devices for on-site monitoring, providing flexible solutions for enhancing workplace safety.
The application uses a custom dataset annotated with tools like Roboflow to train a YOLO model. This tailored approach enhances the model's ability to accurately detect specific objects or features relevant to the project.
This project showcases a custom YOLOv5 model designed to detect safety violations in real-time CCTV footage. It effectively identifies workers lacking helmets, vests, or goggles, serving as a vital tool to enhance workplace safety and prevent accidents.
Key Features:
Benefits:
Deployment: The model can be seamlessly integrated into existing CCTV systems or deployed on edge devices for on-site monitoring, providing flexible solutions for enhancing workplace safety.
This project showcases a custom YOLOv5 model designed to detect safety violations in real-time CCTV footage. It effectively identifies workers lacking helmets, vests, or goggles, serving as a vital tool to enhance workplace safety and prevent accidents.
Key Features:
Benefits:
Deployment: The model can be seamlessly integrated into existing CCTV systems or deployed on edge devices for on-site monitoring, providing flexible solutions for enhancing workplace safety.
The application uses a custom dataset annotated with tools like Roboflow to train a YOLO model. This tailored approach enhances the model's ability to accurately detect specific objects or features relevant to the project.
