This article explores how Reezocar uses computer vision and AWS services to detect car damage, price used vehicles, and support circular economy goals by extending vehicle lifespans.

• Computer vision detects car damage using convolutional neural networks and machine learning algorithms
• Reezocar combines synthetic datasets from 3D CAD models with real-world annotated images via SageMaker Ground Truth
• GPU-accelerated P4d instances process damage detection using AWS Batch, Step Functions, and SQS
• Damage severity classified as scratched (12%), damaged (60%), or wrecked (100%) repair costs
• System achieves 86.7% precision with 6% mean absolute percentage error in pricing
• Reezocar has prevented 30,000 tons of landfill waste and planted 33,000 trees through refurbishment
• Extended vehicle lifespan by up to 5 years using approximately 5% of manufacturer's suggested retail price

Computer vision enables accurate damage assessment for used car pricing, supporting sustainability by extending product lifecycles and reducing waste in secondary markets.