PPE Detection with YOLOv11
This model is a specialized version of YOLOv11, fine-tuned to detect Personal Protective Equipment (PPE) in industrial and construction environments. It is designed to enhance occupational safety by monitoring the use of helmets and safety vests in real-time.
🚀 Quick Start (Usage)
You don't need to download the weights manually. You can load and run the model directly using the Python code below:
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# Downloading and loading the model
model_path = hf_hub_download(repo_id="melihuzunoglu/ppe-detection", filename="best.pt")
model = YOLO(model_path)
# Run inference
results = model.predict(source="construction_site.jpg", conf=0.25, save=True)
✅ Supported Classes (Labels)
The model follows a hierarchical detection strategy for higher accuracy:
Human: Detects the person/worker as the primary anchor.
Helmet: Detects safety helmets worn on the head.
No-Helmet: Specifically identifies heads without safety helmets.
Vest: Detects high-visibility safety vests.
Note: For "No-Vest" detection, the logic is based on the absence of a vest label within a detected human bounding box.
📊 Model Information
Architecture: YOLOv11 (Ultralytics)
Task: Object Detection (PPE Compliance)
Input Resolution: 640x640 pixels
Training Strategy: Hierarchical labeling (Human as anchor) to reduce false negatives in complex backgrounds.
🎯 Target Applications
Construction Sites: Real-time monitoring of helmet and vest compliance.
Industrial Plants: Automated safety audits for manufacturing floors.
Mining & Logistics: Ensuring worker visibility and protection in hazardous zones.
Safety Training: Providing visual feedback during safety drills.
🛠 Training Details
The model was trained using the Ultralytics framework. The dataset was meticulously curated and pre-processed via Roboflow, utilizing a hierarchical approach where equipment is detected in relation to the human figure. This ensures that the model focuses on the equipment's visual features while maintaining spatial awareness of the worker.
👤 Developer
Author: Melih Uzunoğlu Linkedin
Framework: Ultralytics YOLOv11
Dataset Source: Roboflow
Disclaimer
This model is developed for educational and research purposes. While it provides high-accuracy detections, it should be used as a supplementary tool alongside professional safety inspections in critical workplace environments.
- Downloads last month
- 854