Advanced automated procedures replace traditional, manual vehicle inspections.

The availability of powerful computing hardware and dramatic advanced in AI and software technologies have made it possible to conduct highly reliable and accurate vehicle inspections that generate reports in real-time. (Automotive World)

AI-enabled vehicle assessments

New vehicle inspection processes that use huge database libraries, powerful computers, and AI and machine learning are now able to replace traditional manual inspections and achieve better performance, higher efficiency, and lower costs.

AI and machine learning models assimilate and learn from millions of vehicle images. In real-time, they can instantly analyze vehicle images and spot damages that would be impossible for a human inspector to detect.

These models can be run by automobile companies at multiple locations and used for cause and effect analysis to pinpoint whether damages are taking place in their plants, transit logistics, or during customer use. Analysis can produce patterns of occurrence that can be investigated and remedied.

This can help reduce warranty costs, in-transit damage claims, and rework costs within the manufacturing facility itself.

DeGould automated vehicle inspections

DeGould has built up over the years a library of millions of vehicles that serves as a dataset for training AI models.

When combining this data with our neural network technology, dedicated imaging techniques, and image processing, it becomes possible to generate accurate and reliable vehicle damage detection assessment reports.

As more vehicles are inspected, the database expands and the AI models become more efficient.

“Operatives can see damage reports and highlighted areas almost immediately post-inspection, allowing them to carry out additional checks,” says DeGould.

The company can carry out these checks at the time of the vehicle’s plant handover, and thereafter throughout the logistics chain, to pinpoint damages, defects, or even non-conforming parts.

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Image credit: DeGould