Corrosion Annotator
Intelligent Corrosion Detection & Segmentation System
Deep learning-powered system for automated corrosion detection and segmentation in visual inspection images. Uses U-Net architecture with ResNet34 backbone to identify and quantify corroded areas with high accuracy.
Problems Solved
- ×Subjective visual inspection assessments
- ×Missed corrosion in hard-to-see areas
- ×Inconsistent corrosion severity rating between inspectors
- ×Time-consuming manual image analysis
- ×Difficulty quantifying corrosion extent
- ×No historical comparison capability
Solution & Key Features
- ✓Deep Learning Model: U-Net with ResNet34 encoder
- ✓Pixel-Level Segmentation: Precise corrosion boundary detection
- ✓Quantification: Automatic corrosion area and percentage calculation
- ✓Severity Classification: Mild, moderate, severe corrosion grading
- ✓Batch Processing: Analyze hundreds of images automatically
- ✓Multi-Source Support: Works with photos from phones, drones, cameras
- ✓Training Pipeline: Continuously improves with more data
- ✓Visualization: Color-coded heatmaps and overlay masks
Business Value
Efficiency
Process 100+ inspection photos in minutes vs hours of manual review
Quality
95%+ accuracy in corrosion detection, reducing human error
ROI
Reduces inspection time by 30-40% on large visual campaigns
Overall Impact
Corrosion Annotator augments human inspectors with AI-powered insights, catching corrosion that might be missed and providing objective, quantifiable data for asset integrity decisions. It's particularly valuable for large-scale inspection campaigns with thousands of images.
Tech Stack
Project Info
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