ToteCheck is highly accurate and can detect the smallest of items inside totes such as a single grain of sugar. The intelligent deep learning system can also distinguish flat objects or clear liquids, on the base of the tote, such as water. This is important for ensuring food spillages are not missed to prevent meal kit contamination.
Over time, plastic totes can and will get damaged and change appearance as a result of general wear and tear. Because traditional vision systems operate on a strict pass or fail system, they cannot accommodate for acceptable variations caused by natural degradation of the plastic, leading to false rejects that get cleaned unnecessarily.
The deep learning inside ToteCheck learns what natural tote degradation looks like over time – so it can ignore any changes in the plastic and focus instead on any food items or spillages.
As well as being highly accurate, the system can be quickly modified to carry out other inspections in future, such as tote damage.

