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Yolov8m Hard Hat Detection

Developed by keremberke
A YOLOv8m-based hard hat detection model for identifying whether safety helmets are worn in images.
Downloads 550
Release Time : 1/29/2023

Model Overview

This model is an object detection model based on the YOLOv8 architecture, specifically designed to detect whether safety helmets are worn in images, suitable for scenarios such as construction site safety monitoring.

Model Features

High-Precision Detection
Achieves a mAP@0.5 accuracy of 0.811 in hard hat detection tasks.
Real-Time Performance
Optimized inference speed based on YOLOv8 architecture, suitable for real-time applications.
Easy to Use
Provides a simple Python API for quick integration into existing systems.

Model Capabilities

Image Analysis
Object Detection
Hard Hat Recognition

Use Cases

Construction Site Safety Monitoring
Hard Hat Wearing Detection
Automatically detects whether construction site personnel are wearing hard hats to ensure safety compliance.
Can identify personnel not wearing hard hats in real-time and issue timely alerts.
Industrial Safety
Safety Compliance Inspection
Used for safety compliance inspections in factories or construction sites.
Automatically generates compliance reports, reducing manual inspection costs.
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