YOLOv5n Construction Safety Model
| Property | Value |
|---|---|
| Framework | PyTorch (YOLOv5 v7.0.6) |
| Task | Object Detection |
| Performance | mAP@0.5: 0.365 |
| Downloads | 1,109 |
What is yolov5n-construction-safety?
The yolov5n-construction-safety is a specialized object detection model designed to identify safety-related objects and hazards in construction environments. Built on the lightweight YOLOv5n architecture, it provides efficient real-time detection capabilities while maintaining reasonable accuracy with a mAP@0.5 of 0.365.
Implementation Details
This model is implemented using YOLOv5 version 7.0.6 and PyTorch. It features configurable inference parameters including confidence thresholds, IoU thresholds, and multi-label detection capabilities.
- Configurable confidence threshold (default: 0.25)
- Adjustable IoU threshold (default: 0.45)
- Support for test-time augmentation
- Maximum detection limit of 1000 objects per image
Core Capabilities
- Real-time construction safety object detection
- Easy integration with Python applications
- Support for both single image and batch processing
- Flexible model parameter adjustment
- Compatible with custom dataset training
Frequently Asked Questions
Q: What makes this model unique?
This model specifically targets construction safety applications, utilizing the efficient YOLOv5n architecture to provide real-time detection capabilities while maintaining a balance between speed and accuracy. It's particularly valuable for automated safety monitoring in construction environments.
Q: What are the recommended use cases?
The model is ideal for construction site safety monitoring, automated PPE detection, hazard identification, and real-time safety compliance verification. It can be integrated into existing security systems or used for post-analysis of construction site footage.
