⛑️⚒️ Custom object detection for PPE Detection of Construction Site Workers. This repo contains notebook for PPE Detection using YoloV8.
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Updated
Aug 18, 2024
⛑️⚒️ Custom object detection for PPE Detection of Construction Site Workers. This repo contains notebook for PPE Detection using YoloV8.
Real-time PPE detection based on YOLO. Open high-quality dataset.
This project focuses on enhancing construction site safety through real-time detection of safety gear such as helmets and vests worn by workers, as well as detecting the presence of a person.
AI-powered computer vision system for real-time workplace safety monitoring. Detects people and PPE compliance (helmets, vests) using YOLO models with intelligent tracking and MP4 output.
We deliver innovative construction solutions with precision, safety, and quality. From residential to commercial projects, our skilled team ensures timely completion and lasting results. With advanced technology and expert craftsmanship, we build strong foundations for your future.
基于YOLOv8的安全帽佩戴检测系统,集成PyQt5图形界面,支持摄像头实时检测、视频分析和图片识别,适用于建筑工地和工厂安全监管
🔥 基于本质安全理念的建筑施工火灾智能预警交互式演示系统 — AI视觉识别 + 多传感器融合 + LSTM预测,构建"先预防→再控制→后感知"三层防火管控体系
AI 營建工地安全問答系統 — 基於 RAG 的法規合規查詢,完全離線部署(Ollama + Llama 3.1)
CITB Health Safety and Environment Test for Operatives BSL and Specialists
PPE Detection | Computer Vision | Sreamlit | YoloV8 | OpenCV | imageio & imageio-ffmpeg
Book Your CITB Health and Safety Test – Everything You Need to Know
A curated list of HSE (Health, Safety & Environment) software, regulations, datasets, tools, books, training and resources. Curated by SmartQHSE.
My first attempt at creating my website. I will be using a Jekyll Template and hosting on GitHub.
AI-powered PPE compliance and restricted zone monitoring for construction sites. YOLOv8 object detection + DeepSORT tracking with automatic violation logging and snapshots.
Construction and survey form pdf generation
Ern Enerji saha operasyonları için geliştirilmiş, React Native ve .NET 8 tabanlı modern şantiye uygunsuzluk tespit ve takip modülü.
Hard hat detection using ML.NET and TensorFlow with ResNet V2 50 transfer learning. Trains in under 3 minutes with bottleneck caching.
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