tinyML Asia 2022 Video Poster: Automated Yard Processes using TinyML

Automated Yard Processes using TinyML
Hpone Myat KHINE, Data Scientist Intern, SAP

Managing incoming and outgoing trucks at a yard is highly challenging as there are different trucks bound for different locations. Given the competitive pressure in the worldwide markets, it is useful to use professional logistics processes to help reduce complexity. The current approach allows for the Yard Manager to retrieve the tasks associated for a vehicle but it can be repetitive and thus could be further streamlined.

One such way to streamline processes is the automation of the detection of license plate numbers as trucks check-in at the yard. This project demonstrates the utility of the professional yard management software by building computer vision models in embedded devices to create a physical prototype which showcases the automated gate-in process.

Different from traditional computer vision models, the challenge lies in building a low-memory machine learning (TinyML) model which is suitable for embedded devices. In addition, the retrieving and sending of information such as the dangerous goods sign of a truck, and the driver’s next location are also integrated with the yard management software. Finally, the completed prototype will be housed on-site at the company, where it will be used as an innovation showcase.

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