A real-time computer-vision system to monitor construction sites and automatically detect safety violations (e.g., missing PPE, entering restricted zones, unsafe proximity to equipment) using Python, deep learning and video analytics aimed at improving compliance and reducing on-site risk.
OSHA-compliant Electrical Safety Training summary and certification covering PPE, arc flash hazards, and protection devices. (2025)
Helmet Detection Computer Vision Model - A machine learning project that detects helmets in images and video streams using convolutional neural networks. Includes Jupyter Notebook implementations for model training and evaluation, Python utilities for inference, and Docker configuration for containerized deployment.
AI-Driven Industrial Safety: Real-time YOLOv8 PPE detection system featuring asynchronous email alerts, incident evidence capture, and live analytics dashboard.
Public dataset of 2025 federal OSHA HazCom (29 CFR 1910.1200) citations — 130 citations, $172,793 in penalties.
AI-powered construction safety compliance system using YOLOv8 for PPE detection, violation checking, risk scoring, reporting, and Streamlit dashboard.
Frontend development and system integration for a United Airlines cargo safety compliance application. Built the client-facing interface and established data flow between the UI and a computer vision backend.
Daily HOS and DVIR compliance audit tool for Samsara ELD fleets. Flags violations, missing shipping IDs, and drivers approaching the 70-hour weekly limit.
AI-powered Intelligent Safety Compliance Assessment system using Computer Vision and Deep Learning to detect PPE violations and improve workplace safety monitoring.
An unified mobility platform aggregating Uber, Ola & local operators into a single government-compliant app with verified drivers, transparent pricing, and real-time safety features for Indian cities.
Production RAG for Russian regulatory docs (GOST, SNiP, Labour Code) — 7.7/10 correctness, 0.936 faithfulness, 100% out-of-scope abstain, $0.01/query
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