Project Overview
Developed a cutting-edge solution to automatically detect the presence of safety kits in environments like construction sites and industrial facilities. Leveraging advanced image recognition technology, this system helps ensure compliance with safety regulations and fosters a safer workplace by verifying that required equipment—such as helmets, vests, and gloves—is used.

Technologies Used

• Computer Vision
• Deep Learning (Object Detection and Classification)
• Real-Time Image & Video Processing
• Python, TensorFlow, OpenCV, YOLO

Key Features

• Real-time detection of essential safety gear (helmets, vests, gloves, etc.)
• Automated compliance monitoring and alerting
• Integration with surveillance cameras for continuous checks
• Visual reporting and analytics dashboard

Use Cases

• Workplace safety compliance in construction and industrial settings
• Automated safety checks for high-risk areas
• Reducing accidents by enforcing PPE usage
• Incident documentation and audit readiness

Project Outcomes

• Increased adherence to safety protocols
• Reduced workplace incidents and injuries
• Saved labor cost and time on manual inspections
• Comprehensive safety compliance reporting

Challenges Overcome

• Accurate detection in complex, cluttered environments
• Differentiation between various types and colors of safety gear
• Real-time performance with minimal false alerts

Client/Industry

• Construction companies
• Industrial safety managers
• Manufacturing and logistics providers
• Safety compliance solution vendors

Our Role

• Led the full development cycle: dataset creation, model training, system integration, deployment, and continuous improvement.