Aircraft Damage Classification System

Computer vision system for automated damage assessment in aviation maintenance workflows using deep learning classification and natural language generation.

Project Overview

This project addresses quality control challenges in aircraft maintenance by automating damage detection and documentation. The system processes aircraft imagery to classify damage types and generates structured assessment reports for maintenance workflows.

Problem Statement: Manual aircraft damage inspection is time-intensive and requires specialized expertise. This system automates the initial screening process while maintaining assessment quality standards.

Solution:

• Binary classification system (dent vs crack detection)
• Automated damage report generation
• Streamlined integration with existing maintenance protocols

Technical Implementation

Architecture:

• Classification Engine: VGG16 convolutional backbone with custom dense layers
• Documentation System: BLIP transformer model for damage descriptions
• Data Pipeline: Automated preprocessing and batch processing capabilities

Model Specifications:

Foundation: VGG16 (ImageNet pretrained weights)
Custom Layers: Dense(512) → Dropout(0.3) → Dense(512) → Dropout(0.3) → Dense(1)
Optimization: Adam (lr=0.0001), Binary Crossentropy
Input Format: RGB images (224×224 resolution)
Training Strategy: Transfer learning with frozen base layers

Performance Metrics:

• Training methodology: Train/validation/test splits with cross-validation
• Evaluation: Classification accuracy, precision/recall analysis
• Dataset: Aircraft maintenance imagery with binary damage labels
• Processing: Real-time inference capability

Usage

from damage_classifier import DamageClassifier

# Initialize and train system
classifier = DamageClassifier()
data_path = classifier.get_data()
classifier.setup_generators(data_path)
classifier.build_model()
classifier.train()

# Analyze aircraft damage
result = classifier.analyze_image("path/to/aircraft_image.jpg")
print(f"Damage Type: {result['type']}")
print(f"Confidence: {result['confidence']:.3f}")
print(f"Description: {result['caption']}")

System Requirements

tensorflow>=2.8.0
transformers>=4.20.0
pillow>=8.0.0
matplotlib>=3.5.0
numpy>=1.21.0

Dataset Structure

aircraft_damage_dataset_v1/
├── train/
│   ├── dent/     # Training images - structural dents
│   └── crack/    # Training images - surface cracks
├── valid/
│   ├── dent/     # Validation images
│   └── crack/
└── test/
    ├── dent/     # Test images
    └── crack/

Key Results

Classification Performance:

• Binary damage type identification
• Confidence scoring for maintenance decision support
• Batch processing capabilities for large datasets

Documentation Generation:

• Automated damage descriptions
• Structured reporting format
• Integration-ready output formatting

Operational Benefits:

• Reduced manual inspection time
• Standardized damage assessment criteria
• Scalable processing for fleet maintenance

Project Structure

aircraft-damage-detection/
├── README.md                 # Project documentation
├── damage_classifier.py     # Main implementation
├── requirements.txt         # Dependencies
└── results/                 # Generated outputs
    ├── accuracy_plots.png   # Training visualization
    └── sample_results.png   # Classification examples

Technical Approach

Data Preprocessing:

• Image normalization and resizing
• Automated train/validation/test splitting
• Batch processing optimization

Model Development:

• Transfer learning implementation
• Custom classification head design
• Hyperparameter optimization

Integration Design:

• Modular architecture for maintenance system integration
• Standardized API endpoints
• Configurable confidence thresholds

Applications

Primary Use Cases:

• Aviation maintenance quality control
• Fleet inspection automation
• Damage documentation workflows
• Maintenance scheduling optimization

Industry Applications:

• Commercial aviation maintenance
• Military aircraft inspection
• Aircraft manufacturing quality assurance
• Insurance damage assessment

Future Development

Planned Enhancements:

• Multi-class damage classification (beyond binary)
• Severity assessment integration
• Mobile deployment capabilities
• Real-time processing optimization

Scalability Considerations:

• Cloud deployment architecture
• Batch processing optimization
• Integration with maintenance management systems
• Performance monitoring and model updates

Certification

This project was completed as part of the IBM AI Engineering Professional Certificate program, specifically the "Introduction to Deep Learning & Neural Networks with Keras" course. The project demonstrates practical application of:

• Transfer learning techniques
• Computer vision pipeline development
• Multi-modal AI system integration
• Industrial problem-solving approaches

Learning Outcomes

This project showcases proficiency in:

• End-to-end deep learning pipeline development
• Transfer learning with pre-trained models
• Computer vision for industrial applications
• Natural language generation integration
• Professional code organization and documentation
• Real-world problem-solving with AI technologies

Contact

This project demonstrates comprehensive computer vision system development for industrial applications, completed as part of professional AI engineering certification.