defect-detection-ai

AI Defect Detection

Overview

This skill implements deep learning-based defect detection for construction quality control. Analyze images and video to automatically identify structural and surface defects, classify severity, and generate inspection reports.

Detectable Defects:

• Concrete: Cracks, spalling, honeycombing, efflorescence
• Steel: Corrosion, weld defects, deformation
• Masonry: Mortar deterioration, displacement
• Finishes: Surface defects, coating failures
• MEP: Insulation damage, pipe corrosion

Quick Start

import torch
import torch.nn as nn
from torchvision import transforms, models
from PIL import Image
from dataclasses import dataclass
from typing import List, Dict, Tuple
from enum import Enum

class DefectType(Enum):
    CRACK = "crack"
    SPALLING = "spalling"
    CORROSION = "corrosion"
    HONEYCOMBING = "honeycombing"
    EFFLORESCENCE = "efflorescence"
    DEFORMATION = "deformation"
    SURFACE_DAMAGE = "surface_damage"
    NO_DEFECT = "no_defect"

class SeverityLevel(Enum):
    MINOR = "minor"
    MODERATE = "moderate"
    SEVERE = "severe"
    CRITICAL = "critical"

@dataclass
class DefectDetection:
    defect_type: DefectType
    confidence: float
    severity: SeverityLevel
    bounding_box: Tuple[int, int, int, int]  # x1, y1, x2, y2
    area_ratio: float  # Defect area as ratio of image

# Simple classifier using pretrained model
class SimpleDefectClassifier:
    def __init__(self, num_classes: int = 8):
        self.model = models.resnet18(pretrained=True)
        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)
        self.model.eval()

        self.transform = transforms.Compose([
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

        self.classes = list(DefectType)

    def predict(self, image_path: str) -> DefectDetection:
        """Classify defect in image"""
        image = Image.open(image_path).convert('RGB')
        input_tensor = self.transform(image).unsqueeze(0)

        with torch.no_grad():
            outputs = self.model(input_tensor)
            probs = torch.softmax(outputs, dim=1)
            confidence, predicted = torch.max(probs, 1)

        defect_type = self.classes[predicted.item()]

        return DefectDetection(
            defect_type=defect_type,
            confidence=confidence.item(),
            severity=self._estimate_severity(confidence.item()),
            bounding_box=(0, 0, image.width, image.height),
            area_ratio=1.0
        )

    def _estimate_severity(self, confidence: float) -> SeverityLevel:
        if confidence > 0.9:
            return SeverityLevel.CRITICAL
        elif confidence > 0.7:
            return SeverityLevel.SEVERE
        elif confidence > 0.5:
            return SeverityLevel.MODERATE
        else:
            return SeverityLevel.MINOR

# Usage
classifier = SimpleDefectClassifier()
# result = classifier.predict("concrete_image.jpg")
# print(f"Defect: {result.defect_type.value}, Confidence: {result.confidence:.2%}")