Crash Management AI Model
Objective: Our solution automates car damage detection and streamlines the quoting process for panel beaters. By replacing manual inspections with intelligent image analysis, it reduces processing time and enhances the accuracy of damage evaluations.
Methodology: We curated an extensive image dataset that captures a wide range of car damages—from minor scratches to severe structural impacts. Using state-of-the-art deep learning frameworks like TensorFlow combined with robust image processing via OpenCV, our computer vision models were trained and fine-tuned with real-world crash scenarios. This rigorous validation process ensures that our system performs reliably even under diverse lighting and environmental conditions.
Solution: Our custom AI system automatically analyzes uploaded car images to:
- Detect Damage: It uses convolutional neural networks to locate and segment damaged areas.
- Classify Severity: The system categorizes the extent of the damage and quantifies it with precision.
- Generate Incident Reports: Detailed descriptions and preliminary repair cost estimates are produced, facilitating faster insurance processing and more accurate repair quoting.
Impact:
- 50% Reduction in Quoting Time: Accelerated damage assessment means quotes are delivered significantly faster.
- 35% Increase in Assessment Accuracy: Enhanced precision in damage detection leads to fairer, more reliable estimates.
- Improved Customer Satisfaction: Faster processing and reliable assessments translate to a better overall experience for policyholders and repair service providers.
Technologies:
- Computer Vision: Advanced detection and segmentation of damage areas.
- Python & OpenCV: For efficient image preprocessing and analysis.
- TensorFlow: To develop and train deep learning models that accurately assess damage.
- AWS: Scalable cloud infrastructure for real-time processing and deployment.