World premiere at the Unikai terminal in Hamburg: digital vehicle handover inspections facilitated by automated carpodX system

The Port of Hamburg is the first in the world to use the technical innovation carpodX for vehicle inspection. HHLA subsidiary Unikai is using the system developed by the C.A.R. Consulting Group for a thorough, automated check of new and used vehicles before they are shipped via its RoRo facility.
Hartmut Wolberg, Managing Director of Unikai: “After a successful test phase, we are now using the carpodX in the normal course of vehicle inspection. The system documents every detail of the vehicles that roll through the system during normal operation. This allows us as a port logistics company to concentrate on handling instead of taking thousands of manual reports and painstakingly archiving them.”
At Hamburg’s multi-purpose terminal O’Swaldkai, a 20-foot container was set up that can be passed through like a tunnel. Within three seconds, 43 cameras take more than 4,000 photos of the passing vehicles. Every area is completely documented, from the underbody to the roof rails.
Timo D. Pasila, Managing Director of the C.A.R. Consulting Group: “CarpodX is a fully-automated digital condition capture system that can be integrated extremely well into any continual vehicle handling process and is much faster than the conventional visual inspection method. The vehicle does not need to stop, the high definition quality images are more accurate, and our digital inspection container can be placed wherever it is required. This technology also facilitates a more transparent handover procedure whereby eventual damages are assigned to the correct liable party. We are thereby making the vehicle handover inspection and the claims process more fair. Moreover, the carpodX unit is mobile, “plug and play” and robust, and its capacity is practically unlimited. The vehicles move through carpodX during ongoing operations so that there is no reduction in productivity.”
In addition, the photos taken by carpodX are used in a second step to train artificial intelligence through “deep learning”. The goal is to develop AI software that automatically detects and reports damage to vehicles. The system currently in use has to check to see which of the photos shows actual damage – with the help of terminal workers. Through the constant comparison of image and reality, the quality of the automatic image recognition continually improves.
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