Smock Detection Project Overview The Smock Detection Project aims to develop an automated system for detecting the presence of smocks (protective clothing) in images or video frames, commonly used in industrial or healthcare environments. This project leverages computer vision and machine learning techniques to ensure compliance with safety standards.

Features Real-time detection: Capable of identifying smocks in live video streams or pre-recorded footage. High accuracy: Uses advanced machine learning models to ensure reliable detection. Customizable: The system can be trained on different datasets for various environments (e.g., factories, hospitals). Scalable: Can be integrated into larger safety compliance monitoring systems.

Technologies Used Programming Language: Python Framework: TensorFlow/PyTorch (or specify whichever is used) Libraries: OpenCV, NumPy, Pandas, etc. Model Type: Convolutional Neural Networks (CNN), Object Detection Models (YOLO, Faster R-CNN, etc.)# Repo5