Damage Detector
Insurance
Overview
Real-world damage detection from videos/photographs of vehicles being in an accident. Insurance companies have to deal with a lot many claims daily & have to employ surveyors to deal with estimations of cost. Which in the result is prone to human error & may result in some discrepancies. To avoid such cases, we were tasked to handle the damage detection part for 4 wheelers.
Technology
- Deep Learning
- Transfer Learning
- Neural Networks
- Computer Vision
- Image Processing
Language
- Python
Key Technical Challenges:
- Working with all types of damages like glass breakage, doors slammed, dents, scratches to name a few.
- Annotating the images & getting to a sufficient amount of data points for training
- Creating clustered environment for training due to huge processing taken by image kind of data
- Quick inference for production system.
- Making a generalized model for all categories of 4 wheelers
Business + Technical Points:
- Quick response from trained model
- Scalable & adaptable model for heavy vehicles
- Easy training pipeline & responsive dashboard.
Result:
- We were able to identify & meet requirements with 92% accuracy.
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