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Keeping an eye on vehicle damage with AI

Amir Hever, CEO of UVeye, provides an example of how automated solutions powered by AI can maximise accountability in the automotive supply chain.

The European automotive industry is an extremely valuable sector, with millions of vehicles – worth billions of euros – manufactured and shipped internationally every year. 

According to the European Automobile Manufacturers Association (ACEA), which represents the 16 major Europe-based car, van, truck and bus manufacturers, 19.2 million vehicles were produced in the European Union in 2018 alone. As for the value of automotive trade, the 4.2 million vehicles that were imported to the EU in 2018 were collectively worth €54 billion. Even greater are the export figures – in the same year, the EU exported 6.1 million vehicles around the world, worth an extraordinary €138.4 billion. 

En route

What’s more, such valuable products are all subject to a detailed logistics process from factory to dealership. Once a vehicle is finished, has been through initial quality control and is rolled off the factory production line, it begins a long journey with several checkpoints. At every stage, the vehicle is handed over to a new party and must be inspected for any potential damage. 

After being stored in the factory compound, the vehicle is inspected and loaded onto a truck trailer or rail carriage. If it’s going to be shipped overseas, then it is inspected once it arrives at the port. It is inspected again before it boards the ship, and once more when it arrives at its destination. Before being placed on another truck trailer or rail carriage en route to the vehicle processing centre (VPC), the vehicle is inspected yet again. Two more inspections are carried out as the vehicle arrives at, and leaves, the VPC. Once the vehicle reaches the dealership and is unloaded from the truck trailer, it receives one final inspection. 

Even in this typical scenario, it’s clear to see the complexity of the logistics supply chain and the number of points of inspection. The more stages involved, the greater the possibility of damage along the way – and more likely it is that this damage will go unnoticed. Not only does damage result in a loss of revenue for the manufacturer, but it also causes a delay in the delivery of the vehicle to the customer, which has a negative impact on brand perception. 

The topic of accountability is therefore brought into sharp focus. If a vehicle is damaged at any point on its journey from the factory to the dealership, who is responsible for arranging and covering the cost of maintenance? This question is further amplified once you consider both the quantity and value of the goods that are transported. 

Automation brings speed and accuracy

Vehicle manufacturers can typically determine accountability by monitoring the logistics process at every stage. If a handler discovers damage, they are responsible for filing a damage claim and photographs. The assumption is made that the previous link in the logistics chain caused this damage, unless that link can prove that it was already present when they received the vehicle. 

The problem with the traditional damage claims process is that it can be slow and inaccurate. Inspections must be manually carried out, which involves the risk of overlooking faults. In addition, paper reports are slow to fill out and photographs must be carried out with a steady hand and under good lighting. 

Technology and electronic data greatly improve the speed and accuracy of damage incident reports, accelerating the entire claims process. AI-powered technology, however, goes one step further, adding the element of automation to the process. 

Many AI based solutions use deep-learning and intelligent algorithms to automatically detect faults, maintenance issues and cosmetic damage on vehicles. UVeye has three product offerings: Helios, Artemis and Atlas. 

Helios inspects the undercarriage of vehicles. With five high-resolution cameras, the system produces a detailed image within three seconds of a vehicle driving over the hardware – at speeds of up to 30 km/h – and a full analysis within 10 seconds. The system provides the user with a detailed view of the complex componentry on a vehicle’s undercarriage. Deep learning algorithms then process the image and alert the user of anomalies that would otherwise go unnoticed by the human eye. 

Artemis is UVeye’s tyre inspection product. The system comprises two tyre scanners that stand at the side of the vehicle while it drives past – effective at speeds up to 20 km/h. In a matter of seconds, Artemis reads and recognises the tyre brand, markings, and technical specifications, as well as crucial safety-related data such as tyre condition, pressure, abrasions and scratches. 

Atlas is a full-body scanner and is used to detect any dents, scratches or other cosmetic issues on the vehicle’s bodywork. 

Everything from the lighting, moisture levels, and other natural conditions can change how an image may appear, so the company’s deep-learning systems are trained to detect and identify anomalies regardless of the situation in the field. 

Informed decisions

By automating the inspection process and eliminating the need for vehicles to remain stationary, AI based solutions ensure that the flow of the logistics chain is continuous and uninterrupted. The systems improve the transparency and objectivity of inspections throughout the logistics process and allow vehicle manufacturers to quickly source the origin of any damage. 

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