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Yolov8s Hard Hat Detection
A hard hat detection model based on YOLOv8s, used to identify whether safety helmets are worn in images.
Downloads 205
Release Time : 1/29/2023
Model Overview
This model is an object detection model based on the YOLOv8 architecture, specifically designed to detect whether personnel in images are wearing safety helmets, suitable for scenarios such as construction site safety monitoring.
Model Features
High-precision Detection
Achieves 83.4% mAP@0.5 on the validation set, accurately identifying safety helmet usage.
Lightweight Model
Based on the YOLOv8s architecture, it maintains high accuracy while offering fast inference speeds.
Easy to Use
Provides a clear Python API, enabling prediction and visualization with just a few lines of code.
Model Capabilities
Image Object Detection
Safety Helmet Recognition
Construction Site Safety Monitoring
Use Cases
Construction Site Safety
Safety Helmet Detection
Automatically detects whether construction site personnel are wearing safety helmets, improving safety management efficiency.
Enables real-time monitoring and generates violation reports
Industrial Monitoring
Safety Compliance Inspection
Used for automated safety compliance checks in factories, construction sites, and other locations.
Reduces manual inspection costs
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