Structural Damage Detector
General purpose
Overview
Real world damage detection from videos/photographs of Infrastructure undergoing construction or structural strength survey or maintenance survey. Manual surveys are prone to corruption & human error which has resulted in huge losses. With much accurate & faster eyes of computer & advancements in devices help us get more detailed data & in a result generate better predictions with better accuracy.
Technology
- Deep 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.
- Creating clustered environment for training due to huge processing time taken by image kind of data
- Real time inference in mobile as well as web application.
- Should work with any kind of background.
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 accurately with 44ms inference time.
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