Streamlining Vehicle Inspection by Leveraging AI-driven solutions

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“Incorporating new technologies will always have an element of disruption, but the best product suites are those that minimize the impact on the existing business—from IT integrations through to operational implementation,” says Radu Rusu, CEO, Fyusion in an exclusive interview with EnterpriseTalk.

ET Bureau: What are the major challenges the automotive industry is facing when it comes to vehicle inspection?

Radu Rusu: In the auto industry, time is money. For instance, imagine a car being traded-in. Between the moment the original owner trades it in and the moment the next owner buys it, the car is inspected numerous times as it makes its way to a wholesaler and backs out to another dealer.

Each of those inspections costs money and cuts directly into the profitability of the car.

Every day of idle time added by inspections costs money because real estate on car lots is an expense, and the faster the car moves off the lot, the better.

Every time an inspection has to be redone—which is around 15 percent of the time with human inspectors—it adds hard costs and time to the process.

The need to send out in-person specialists for inspections slows things down and ensures the entire process will be expensive while still failing to deliver consistently accurate results due to the inevitability of human error.

When automotive enterprises speed up this process, make it more accurate and reduce costs, everyone wins, including their customers.

ET Bureau: How can the automotive industry leverage artificial intelligence to upgrade its process of vehicle inspection? 

Radu Rusu: There are three main ways AI can improve the process of vehicle inspection: The up-front part of capturing images, the accuracy of reporting, and the speed with which this data becomes available to customers.

If done well, the end result is lower costs and shortened sales cycles.

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In terms of capturing images, AI-driven 3D can provide imagery much closer to the way the human eye sees things. Most solutions are mixing automation (AI) and a human crosschecker on the backend, which is popularly called a human in the loop. Both the AI and the human benefit from 3D visuals rather than 2D images—in essence, examining the car from an unlimited number of angles to draw the most accurate conclusion possible.

Moreover, even if an inspector is on-site, having great 3D images of the vehicle can benefit the process and enable the inspectors to better check their own work.

Leveraging AI on the back end provides a deeper understanding of visual data beyond what human intelligence can analyze, and it does so in a consistent and unbiased way, day after day. AI doesn’t need sick days, is never in a bad mood and will never need a cup of coffee to be effective. Unlike people whose performance fluctuates, AI constantly learns with each image that is processed and, over time, will only become better at identifying damages. Running AI on the back end makes a huge difference in terms of speed and cost.

Finally, with AI-driven processes, reporting happens really, really fast. Cell phones have become so powerful that someone could do most of the processing for an AI-based automotive inspection on their device and have results instantly.

This does not mean completely removing humans from the inspections process. Artificial intelligence is often best when partnered with human intelligence. For instance, once a condition report is generated with Fyusion Inspect, an expert can validate it before being released to the customer.

With AI doing most of the work and human intelligence as a final reviewer, the quality of the end report is likely to exceed that of a report generated by a professional inspector on-site.

Depending on the use case, there are many ways enterprises can leverage AI in the inspections process.

ET Bureau: Inaccuracy in vehicle inspection is a major problem in today’s many enterprise software. What type of solution organizations should seek to address this problem? 

Radu Rusu: When looking for AI-driven inspections solutions, finding one based on 3D imagery rather than flat 2D photos is really important.

AI is only as effective as the data that’s fed into it, and 2D photos can easily hide or distort damages.

3D images capture the world much as the human eye does and enables AI to analyze damage the same way as a person would, by moving around it from every angle. 3D-based solutions provide improved accuracy and require less human intervention. And in this industry, delays are costly.

ET Bureau: How can automotive enterprises seamlessly integrate the necessary product suites without disrupting their operations or workflows?

Radu Rusu: Incorporating new technologies will always have an element of disruption, but the best product suites are those that minimize the impact on the existing business—from IT integrations through to operational implementation.

The key points that enterprises should look for are ease of integration, hardware requirements, and simplicity of capture processes.

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Automotive enterprises should identify solutions that are simple to integrate, whether as an SDK or fully baked mobile application.

Any solution should work with their existing smartphone and tablet hardware in order to reduce or avoid additional capital investment. Plus, when automotive enterprises decide to switch hardware in any facet of the business, training time and the learning curve is always higher than planned.

To that end, enterprises should seek a solution that is simple to operate in the field. It should simplify and improve the entire inspection flow, from an intuitive capture process to delivering the final audited condition reports.

The CEO and Co-Founder of Fyusion, President of Open Perception, and a world-renowned expert in 3D data processing with almost 20 years of experience in the field. Radu was named Top 100 Most Intriguing Entrepreneurs in 2016 by Goldman Sachs, was awarded the IEEE RAS Early Career Award in 2013 for his contributions to the field of 3D Computer Vision, and won the Open Source Software (OSS) World Challenge in 2011 with PCL.