Case Study
Mobile damage detection system
We developed an AI-powered mobile app that accurately detects and classifies mobile phone damage, ensuring efficiency, reliability, and fraud prevention.
We developed an AI-powered mobile app that accurately detects and classifies mobile phone damage, ensuring efficiency, reliability, and fraud prevention.
The project aimed to replace manual inspection with an AI-driven model capable of detecting defects such as broken screens, dents, and spots on mobile phones. The solution needed to classify damage as minor or major, influencing the mobile’s resale value. Additionally, fraud detection mechanisms were vital to ensure the system's credibility and accuracy in real-world scenarios.
Our client, a mobile refurbishing leader, needed an AI-driven solution to automate damage detection, improve pricing accuracy, and prevent fraud in assessing mobile phones’ screen and back panel conditions.
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Abhay
Head of Mobile Development
> Developing precise models for screen and back panel defect detection.
> Accurately classifying defects as minor or major.
> Ensuring the system worked under varying lighting conditions.
> Implementing fraud prevention measures, including skin detection, glare detection, and excess brightness checks.
> Ensuring real-time performance and user-friendliness.
The system was built using advanced AI and image processing techniques. For screen defects, adaptive thresholding, shape detection, and morphological operations were employed, while Pytesseract and Mask RCNN addressed back panel issues. A mobile app with Flask ensured accessibility, while tools like OpenCV, TensorFlow, and AWS enabled real-time performance. Fraud detection mechanisms added layers of security, ensuring reliability.
The automated damage detection system successfully identified and localized defects, categorizing damage severity with high accuracy. The mobile app operated seamlessly in various lighting conditions, enhancing usability. Fraud prevention measures ensured the system’s integrity. Overall, the project delivered a reliable, efficient, and secure tool, revolutionizing the mobile refurbishing process.
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