Paved_Road_pothole_detection

Paved road pothole detection - Facility

📝 Overview
This model is trained to detect helmet usage for Personal Protective Equipment (PPE).
It is designed for workplace safety monitoring, compliance checks, and real-time Paved Road Detection in CCTV or video streams or uploaded photo.

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🏗️ Model Details

• Model Architecture: YOLOv12s
• Base Model: Ultralytics/YOLO12s
• Training Date: 13 August 2025
• Created Date: 13 August 2025
• Pipeline Tag: object-detection

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📂 Dataset

• Source: Roboflow
• Workspace: fatemews
• Project: pothole-detect-yolov9
• Processing: Preprocessed and augmented using Roboflow pipeline

Classes:

1. pothole

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📊 Use Cases

This model can be deployed for:

• 🎥 Real-time video detection
• 🏭 Workplace compliance monitoring
• 📡 Integration with edge devices (CCTV, Jetson Nano, Raspberry Pi, etc.)

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🚀 Deployment

Example usage in Python (Ultralytics):

from ultralytics import YOLO

# Load the trained model
model = YOLO("helmet-detection-yolov12m.pt")

# Run inference on an image
results = model.predict("test_image.jpg", show=True)

# Run on a video or webcam
results = model.predict(source=0, show=True)  # 0 = webcam