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RoxieRoller
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QuickFixIt-model

Object Detection
yolo
yolov8
pothole-detection
road-damage
defect-classification
smart-cities
civic-tech
india
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docs: update model card with 4-way defect classifier and anti-gaming repair verification details
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metadata 
license: mit
tags:
  - yolo
  - yolov8
  - object-detection
  - pothole-detection
  - road-damage
  - defect-classification
  - smart-cities
  - civic-tech
  - india
datasets:
  - roboflow-indian-pothole
  - municipal-road-defect-benchmark
pipeline_tag: object-detection

🛣️ 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

  1. 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.

  2. 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