An advanced expert system for identifying and classifying asphalt pavement cracks with comprehensive data visualization and analytics capabilities.
- Multi-parameter Detection: Analyzes cracks based on length, width, intensity, density, and location
- Confidence Scoring: Provides reliability scores (0-100%) for each detection
- Multiple Detection: Can identify multiple crack types from a single set of parameters
- Smart Classification: Supports 6 major crack types with detailed characteristics
- Distribution Charts: Pie charts showing crack type percentages
- Confidence Histograms: Visualize detection reliability patterns
- Location Heat Maps: Identify crack concentration zones on road width
- Time Series Analysis: Track detection trends and patterns over time
- Severity Analysis: Compare severity levels across crack types
- Interactive Dashboard: Multi-tab analytics view with all visualizations
- Alligator Cracks - Interconnected cracks resembling alligator skin
- Block Cracks - Rectangular pattern cracks from thermal shrinkage
- Edge Cracks - Parallel cracks near pavement edges
- Longitudinal Cracks - Cracks parallel to road centerline
- Reflection Cracks - Cracks reflecting from underlying layers
- Slippage Cracks - Crescent-shaped surface tears
- 📊 Real-time Analytics - Interactive charts and graphs with zoom/pan
- 📈 Trend Analysis - Historical data visualization
- 🗺️ Spatial Analysis - Road position-based crack mapping
- 📊 Batch Processing - Analyze multiple cracks from file input
- 📝 History Tracking - Maintains detection history for analysis
- 💾 Export Functionality - Save results and charts
- 📚 Built-in Database - Comprehensive crack information repository
- 🎯 Location-Aware Detection - Considers crack position on road width
- 🛠️ Repair Recommendations - Detailed repair methods based on severity
- Python 3.8 or higher
- tkinter (usually comes with Python)
- matplotlib 3.5+ (for analytics and visualization)
- numpy 1.20+ (for data processing)
pip install matplotlib numpy- Clone the repository:
git clone https://github.com/yourusername/asphalt-crack-detection.git
cd asphalt-crack-detection- Run the application:
python crack_detection.py- Launch the application - Demo data is automatically generated
- View instant statistics - Click "Quick Analytics" button
- Explore visualizations - Use Analytics menu:
- Start with Distribution Chart for overview
- Check Location Heat Map for problem areas
- Review 8000 Time Series for trends
- Open full dashboard - Analytics → Full Analytics Dashboard
- Make a detection - Enter parameters and click "Detect Cracks"
- Export charts - Right-click any chart to save as image
- Launch the application
- Enter crack parameters:
- Length (mm): Crack length measurement
- Width (mm): Crack width measurement
- Intensity: Low/Medium/High severity
- Density: Low/Medium/High concentration
- Location (%): Position from left edge (0-100%)
- Click "Detect Cracks" to analyze
- Review results with confidence scores and recommendations
| Parameter | Description | Range | Example |
|---|---|---|---|
| Length | Crack length in millimeters | > 0 | 1500 mm |
| Width | Crack width in millimeters | > 0 | 50 mm |
| Intensity | Severity level of the crack | L/M/H | Medium |
| Density | Concentration of cracks in area | L/M/H | High |
| Location | Position on road (0=left edge, 100=right edge) | 0-100% | 25% |
Create a text file with crack data (space-separated values):
length width intensity density location
1500 50 M H 25
3000 100 H H 50
500 10 L L 90
Then use File → Process Batch File to analyze all entries at once.
-
Distribution Chart
- Visual breakdown of crack type percentages
- Pie chart with color-coded segments
- Access: Analytics → Distribution Chart
-
Confidence Histogram
- Shows reliability distribution of detections
- Color-coded by confidence level (Green >70%, Orange 50-70%, Red <50%)
- Displays mean confidence line
- Access: Analytics → Confidence Histogram
-
Location Heat Map
- Road visualization showing crack concentrations
- Bubble size indicates frequency
- Bubble color indicates severity
- Helps identify problem zones (edges, center, wheel paths)
- Access: Analytics → Location Heat Map
-
Time Series Analysis
- Track detection trends over time
- Multiple crack types on single graph
- Identify seasonal patterns
- Access: Analytics → Time Series Analysis
-
Severity Analysis
- Grouped bar charts by crack type
- Compare Low/Medium/High severity distributions
- Count labels on each bar
- Access: Analytics → Severity Analysis
Access comprehensive multi-tab view:
- Analytics → Full Analytics Dashboard
- Combines all visualizations in one window
- Three tabs: Overview, Location Analysis, Time Analysis
- Export individual charts or entire dashboard
- Click "Quick Analytics" button for instant summary
- Shows total detections, type distribution, and severity breakdown
- No need to open separate windows
The system uses a sophisticated weighted scoring algorithm:
-
Parameter Matching (25% weight each):
- Length range validation
- Width range validation
- Intensity correlation
- Location preference scoring
-
Confidence Calculation:
- Base score multiplication
- Density factor adjustment
- Final score normalization (0-100%)
-
Multi-Detection Support:
- Simultaneous crack type identification
- Sorted by confidence level
- Threshold-based filtering (>30%)
Repair recommendations are determined by a 3x3 matrix considering:
- Intensity (Low/Medium/High)
- Density (Low/Medium/High)
Each combination provides specific repair guidance from "Do nothing - monitor" to "Full reconstruction".
asphalt-crack-detection/
│
├── crack_detection.py # Main application file
├── README.md # This file
├── sample_data.txt # Sample batch processing file
└── results/ # Output directory for exports
| Feature | v1.0 | v2.0 |
|---|---|---|
| Detection Accuracy | Simple thresholds | Weighted scoring algorithm |
| Confidence Scores | No | Yes (0-100%) |
| Multiple Detections | Limited | Full support |
| User Interface | Basic | Professional GUI |
| Input Validation | None | Comprehensive |
| Batch Processing | Basic | Advanced with error handling |
| Documentation | Minimal | Extensive |
| Code Quality | Procedural | Object-oriented |
| Error Handling | Limited | Comprehensive |
| Export Options | None | Multiple formats |
| Data Visualization | None | 5+ chart types |
| Analytics Dashboard | None | Multi-tab dashboard |
| Historical Analysis | None | Time series tracking |
| Pattern Recognition | None | Location heat maps |
| Statistical Reports | None | Quick stats & detailed analysis |
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
- Follow PEP 8 style guide
- Add type hints for new functions
- Include docstrings for all classes and methods
- Test thoroughly before submitting PR
- Detection Speed: <100ms per crack
- Analytics Generation: <2s per chart
- Accuracy: Confidence-based scoring system
- Memory Usage: ~100MB (with analytics)
- Chart Rendering: Real-time with zoom/pan
- Data Capacity: 1000+ detections without performance impact
- Supported OS: Windows, macOS, Linux
- Requires manual measurement input (no image processing yet)
- Detection rules are based on empirical data
- Best suited for standard asphalt pavements
- Analytics require sufficient data (minimum 10-20 detections recommended)
- Time series analysis limited to date-based grouping
The application automatically generates 50 sample detections on startup for:
- Testing analytics features without real data
- Demonstrating visualization capabilities
- Learning the system functionality
To start with a clean slate, simply restart the application and avoid the demo data generation.
- Image processing for automatic measurement
- Machine learning integration for improved accuracy
- Mobile app development
- Cloud-based analysis and storage
- Integration with GIS systems
- Real-time monitoring capabilities
- PDF report generation with charts
- Cost estimation for repairs
- Multi-language support
- API for third-party integration
-
Data visualization and analytics✅ Implemented in v2.0! -
Historical trend analysis✅ Implemented in v2.0! -
Statistical reporting✅ Implemented in v2.0!
- Consistent Measurements: Use the same measurement methodology
- Regular Intervals: Collect data at consistent time intervals
- Complete Information: Fill all parameters for each detection
- Location Accuracy: Be precise with location percentages
- Distribution Charts: Look for dominant crack types requiring attention
- Confidence Analysis: Low confidence may indicate measurement issues
- Heat Maps: Identify systematic problems (e.g., poor edge support)
- Time Trends: Watch for increasing detection rates
- Severity Patterns: High severity concentrations need immediate attention
- Use Time Series for seasonal planning
- Apply Heat Maps for targeted repairs
- Review Severity Analysis for resource allocation
- Export charts for management reports
This project is licensed under the MIT License - see the LICENSE file for details.
- Improved design and implementation for v2.0
- Based on original concept with significant enhancements
- Transportation engineering principles
- Pavement management best practices
- matplotlib for powerful visualization capabilities
- Data analytics concepts for infrastructure management
- User feedback from v1.0
For issues, questions, or contributions:
- Open an issue on GitHub
- Check the built-in Help menu
- Review the user guide in the application
- ✨ Added comprehensive data visualization with matplotlib
- 📊 Implemented 5 types of analytics charts
- 🎯 Added interactive analytics dashboard
- 📈 Included time series analysis for trend tracking
- 🗺️ Created location-based heat map visualization
- 📉 Added confidence score distribution analysis
- 📊 Implemented severity analysis by crack type
- 🎲 Added demo data generation for testing
- 🚀 Improved UI with Quick Analytics button
- 📁 Enhanced menu structure with Analytics section
- Basic crack detection functionality
- Simple GUI interface
- Text-based results
- Manual input only
Note: This system is designed for educational and professional use in pavement management. Always consult with qualified engineers for critical infrastructure decisions.
Version 2.0 - Analytics Edition: This enhanced version includes comprehensive data visualization and analysis tools, enabling data-driven maintenance decisions and strategic pavement management.