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Computer Vision & Deep LearningSystem ID: EVR-VISION-03•5 min read

Workplace Safety & PPE Real-Time Vision AI

Real-time automated safety monitoring system utilizing YOLOv8 and deep learning to detect helmet and PPE compliance across live camera feeds.

Primary Impact:30 FPS Live Stream
Core Stack:YOLOv8Deep LearningComputer VisionPythonPyTorch
Workplace Safety & PPE Real-Time Vision AI
!The Challenge

Operational Friction & Bottlenecks

Industrial worksites, construction areas, and factories struggle with PPE compliance enforcement, creating immense regulatory liability and severe safety hazards.

✓The Evaroid.AI Solution

Engineered AI Architecture

Evaroid.AI deployed a low-latency edge computer vision pipeline based on a customized YOLOv8 model that tracks worker personnel and checks for hardhat compliance in real time.

System Architecture

Technical Execution & Pipeline Flow

EVR-VISION-03 FLOW
1
RTSP video stream ingestion from on-site IP surveillance cameras
2
Frame preprocessing, normalization, and hardware-accelerated batching
3
Custom YOLOv8 object detection inferring worker silhouettes and helmet classes
4
Temporal violation tracking to prevent false alerts from momentary occlusions
5
Instant sound alert trigger and real-time incident screenshot logging to dashboard

Measurable Results & Business Impact

99.1%
PPE Detection Precision
14ms
Inference Latency per Frame
70%
Drop in Worksite Safety Violations

Key Capabilities & Features

Runs at 30+ FPS on edge hardware and standard GPUs
High accuracy across harsh lighting, rain, and dust conditions
Configurable safety hazard exclusion zones and shift schedules
Automated safety compliance reporting and incident playback

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