Automated Screen Damage Detection for Smartphones
Seasia Infotech partnered with a leading refurbished phone enterprise to design an AI-based mobile screen inspection for refurbishment that streamlines quality control for warehouses and repair centers. The client aimed to replace error-prone manual inspections with a mobile screen grading system powered by computer vision, multi-angle CCD imaging, and deep learning models.
The goal: achieve 90%+ accurate phone screen crack detection, reduce operational costs, and scale mobile device quality control across thousands of devices.
Project Overview
The refurbished device market suffers from delays and inconsistencies in manual phone repair inspection systems. Inspectors often miss micro-cracks or grade severity inconsistently, leading to disputes, returns, and slow refurbishment cycles.
Seasia Infotech developed a deep learning screen damage detection solution designed as a scalable automated phone screen crack detection system for warehouses. The system combines multi-angle phone inspection systems, RGB → HSI transformations, and CNN-based object detection systems for smartphone screen defects to accurately classify cracks into minor, moderate, and severe categories.
Project Overview
The refurbished device market suffers from delays and inconsistencies in manual phone repair inspection systems. Inspectors often miss micro-cracks or grade severity inconsistently, leading to disputes, returns, and slow refurbishment cycles.
Seasia Infotech developed a deep learning screen damage detection solution designed as a scalable automated phone screen crack detection system for warehouses. The system combines multi-angle phone inspection systems, RGB → HSI transformations, and CNN-based object detection systems for smartphone screen defects to accurately classify cracks into minor, moderate, and severe categories.
Project Scope & Challenges
The client needed a mobile device quality control solution to automate screen defect detection across different models and volumes, overcoming manual inspection limits.
Subtle Damage
- Cracks visible only at certain angles
Model Variability
- Different sizes and materials
High Throughput
- Inspecting thousands daily
Manual Limitations
- Inconsistent and costly checks
Seasia’s Solution
By engineering an AI-driven mobile device repair automation platform through its Artificial Intelligence services , Seasia transformed phone inspection into a fast, scalable process.
Multi-Angle CCD Camera System for Phone Screen Inspection to capture every defect
RGB → HSI Color Transformation to enhance defect visibility.
Deep Learning & Object Detection models for detecting minor, moderate, and major screen damage automatically.
Customizable Training Tools allowing clients to upload datasets for new models.
Automated Mobile Screen Grading System for severity classification
Technology Stack
Frontend
CCD Cameras
Image Preprocessing TechniquesBackend
Deep Learning
Computer Vision Models (CNNs)APIs / Services
Nanonets API
OCR IntegrationData Processing
RGB → HSI
Noise Reduction
Angle & Lighting CorrectionFrontend
CCD CamerasImage Preprocessing TechniquesBackend
Deep LearningComputer Vision Models (CNNs)APIs / Services
Nanonets APIOCR IntegrationData Processing
RGB → HSINoise ReductionAngle & Lighting Correction
Results & Achievements
90%+ Accuracy AI for phone screen inspection across 200,000+ devices
3× Faster Throughput compared to manual inspections
Cross-Model Adaptability across multiple brands and phone types
Cost Savings via reduced manpower dependency
Reliable Mobile Screen Damage Detection that cut disputes and returns
Transforming Refurbished Phone Quality Control with AI
Seasia’s automated screen inspection demonstrates how mobile screen damage detection powered by AI can reshape the used phone refurbishment technology sector. By combining deep learning, multi-angle phone inspection systems, and customizable training datasets, Seasia delivered a solution that accelerates inspections, boosts accuracy, and ensures consistent grading at scale.
What They Say
Simmi Walia
CEO at Realesta
An Esteemed Client
New York, USA
Mrs. Arshpreet Kaur
Director, WELRM
Nigel Teags
CEO - TrackNinja
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