YOLOv5m Construction Safety Detection Model
| Property | Value |
|---|---|
| Framework | PyTorch (YOLOv5) |
| Library Version | 7.0.6 |
| Performance | mAP@0.5: 0.374 |
| Downloads | 869 |
What is yolov5m-construction-safety?
This is a specialized computer vision model based on YOLOv5m architecture, designed specifically for construction site safety monitoring. The model is trained to detect various safety-related objects and violations in construction environments, helping maintain workplace safety standards through automated visual inspection.
Implementation Details
The model is implemented using the YOLOv5 framework version 7.0.6 and is built on PyTorch. It features customizable inference parameters including confidence thresholds, IoU thresholds, and detection limits. The model accepts images of 640x640 resolution and can process both single images and batches.
- 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
- Support for both single-image and batch processing
- Easy integration with existing PyTorch workflows
- Flexible deployment options with pip installation
- Fine-tuning capabilities on custom datasets
Frequently Asked Questions
Q: What makes this model unique?
This model specifically targets construction safety applications, with optimizations for detecting safety-related objects in construction environments. It offers a balance between accuracy and speed using the medium-sized YOLOv5 architecture.
Q: What are the recommended use cases?
The model is ideal for automated safety monitoring in construction sites, real-time violation detection, PPE compliance checking, and integration into existing construction site monitoring systems.
