YOLOv8n Hard Hat Detection Model
| Property | Value |
|---|---|
| Author | keremberke |
| Framework | PyTorch/Ultralytics |
| Performance | mAP@0.5: 0.83633 |
| Downloads | 3,373 |
What is yolov8n-hard-hat-detection?
This is a specialized object detection model based on the YOLOv8-nano architecture, designed specifically for detecting the presence or absence of hard hats in images. The model achieves an impressive 83.6% mAP@0.5 on validation data, making it reliable for safety compliance monitoring in construction and industrial settings.
Implementation Details
Built using the Ultralytics framework version 8.0.23, this model implements a binary classification system for hard hat detection. It uses advanced neural network architecture optimized for real-time detection with efficient processing capabilities.
- Confidence threshold: 0.25
- IoU threshold: 0.45
- Maximum detections per image: 1000
- Supports both 'Hardhat' and 'NO-Hardhat' classifications
Core Capabilities
- Real-time hard hat detection
- Binary classification between proper and improper head protection
- Optimized for construction site safety monitoring
- Easy integration with Python applications
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in hard hat detection with high accuracy while maintaining the efficiency of the YOLOv8-nano architecture, making it ideal for real-time safety monitoring applications.
Q: What are the recommended use cases?
The model is perfect for construction site safety monitoring, automated PPE compliance checking, and industrial safety applications where hard hat detection is crucial.
