AI-Powered Screen Damage Detection System 
Automated crack and damage assessment at scale
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About
Industry
- Mobile Manufacturing, Device Repair, Insurance
Application Type
- Cloud-Based AI Image Analysis Platform
Core Functionality
- Screen crack detection, damage classification, fraud validation
It is an AI-powered computer vision platform built to assess mobile phone screen damage with speed and precision. Designed for manufacturers, service centers, and insurance providers, the system automates inspection by analyzing device images for cracks, scratches, and glass imperfections.
Traditional manual inspection methods were slow, inconsistent, and prone to subjective judgment. The mobile screen damage detection system replaces this with a scalable, cloud-deployed solution that leverages deep learning models to classify damage severity accurately.
The system leverages computer vision and deep learning models (VGG + YOLO architectures) to detect cracks, scratches, and glass imperfections from device images.
The platform integrates seamlessly with existing enterprise systems, enabling real-time or batch-based evaluations while maintaining strict data privacy and auditability.
Results
The deployment of phone screen damage detection using AI delivered immediate operational and financial impact.
Speed
82% reduction in inspection time per device compared to manual assessment.
Accuracy
97.8% precision in damage classification, including crack depth, surface scratch, and glass shatter.
Fraud Control
46 percent reduction in fraudulent insurance claims using automated image validation.
Scale
Enabled parallel processing for batch analysis for 10,000 plus images daily
Efficiency
Parallel inspection without human bottlenecks
The collaboration felt less like vendor engagement and more like a true product partnership.
Challenges
Why It Mattered
Manual screen inspections were slow, inconsistent, and costly. Automated screen damage detection system reduced human dependency and introduced transparency and accuracy into repair and insurance decision-making. It revolutionized how mobile insurers and repair vendors evaluate claims and repair needs with precision and transparency.
Our Approach-
We built a scalable AI vision system that detects, classifies, and validates mobile screen damage in real time.
Our Tools:
AI/ML
- TensorFlow
- Keras
- PyTorch
Model Architecture
- VGG16
- YOLOv5
Data Handling
- OpenCV
- NumPy
- Pandas
Deployment
- AWS Lambda
- EC2
- S3
Integration
- RESTful APIs
- JSON-based data exchange
Before & After
The introduction of screen damage detection system contributed positively across accuracy, speed, and processing capacity.
| Feature / Metric | Before (Manual Process) | After (AI-Powered) |
|---|---|---|
| Average Inspection Time | 5–6 minutes per device | 40–50 seconds per device |
| Damage Detection Accuracy | 70–75% human variability | 97.8 percent consistent |
| Fraudulent Claim Detection | Less than 10 percent | 46 percent reduction |
| Processing Capacity | 100 devices per day | 10,000 plus devices per day |
| Traceability & Audit Logs | Manual spreadsheets | Automated cloud-based logs |
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