🛣️ QuickFix It — AI Road Defect & Repair Verification Model
Fine-tuned YOLOv8n and Multi-Class Road Defect Spatial Pyramid Classifier trained specifically for municipal road maintenance and automated repair verification in Indian urban environments.
Developed as the core AI engine for QuickFix It (GitHub: mohammedhussain06/QuickFix).
🎯 Key Capabilities
4-Way Defect Differentiation:
- Potholes: Asphalt depressions, cavities, and broken paver rims.
- Manhole Collars: Circular/rectangular utility frames, seated covers, and hazardous open vertical shafts.
- Waterlogging: Carriageway flooding, standing water sheets, and poor drainage ponding.
- Cracks: Bituminous fractures, longitudinal tears, and alligator cracking patterns.
Automated Before/After Repair Auto-Verification:
- Anti-Substitution Defense (The Twist): Prevents contractors from gaming the system by photographing an already-fixed road from another location.
- Landmark Alignment: Validates background landmarks (e.g., Metro pillars, stationary vehicles, kerb lines) via SIFT/ORB homography invariance.
- Perceptual Integrity: Detects recycled photo fraud via pHash bitstream collision matching.
- GPS & Compass Verification: Validates repair camera coordinates (<15m) and heading tolerance (<25°).
📊 Model Information
| Parameter | Specification |
|---|---|
| Primary Architecture | YOLOv8n (Ultralytics) + 55-D Spatial Pyramid Forest |
| Model Weights | best.pt (~6.2 MB) |
| Feature Forest | trained_forest.json (Spatial Pyramid Texture & Gradient Ensembles) |
| Training Dataset | 2,281+ Indian road scenes (Roboflow Indian Pothole + municipal ground truth) |
| Precision / Recall | mAP50: 91.4%, Defect Classification Accuracy: 94.2% |
🚀 Quickstart Usage
1. Object Detection (YOLOv8)
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# Download weights from Hugging Face Hub
weights_path = hf_hub_download(repo_id="RoxieRoller/QuickFixIt-model", filename="best.pt")
# Load model & run inference
model = YOLO(weights_path)
results = model.predict(source="road_photo.jpg", conf=0.25)
results[0].show()
2. Full Verification Pipeline Integration
In your backend .env:
HF_MODEL_REPO=RoxieRoller/QuickFixIt-model
YOLO_MODEL_PATH=models/pothole_yolov8/weights/best.pt
👥 Authors & Repository
- Author: Mohammed Hussain (@mohammedhussain06)
- Platform Repo: github.com/mohammedhussain06/QuickFix
- Hugging Face Model: huggingface.co/RoxieRoller/QuickFixIt-model