To achieve this deep learning, Sick is harnessing neural networks. Compared to the classical process for developing algorithms, which is characterised by manual development of feature representation, a neural network is trained to optimise its task and can be retrained with new data to adapt to new circumstances.
To train the networks, Sick is collecting and assessing thousands of images and examples. The new deep learning algorithms it generates will be implemented on sensors such as smart cameras, making them failsafe and directly available.
One application of Sick’s deep learning technology is to detect whether sorting trays in logistics hubs are actually loaded with only one object.
By implementing deep learning in certain types of sensors, Sick says it is taking its AppSpace platform to the next level. It envisages artificial intelligence being added eventually to devices such as inductive proximity sensors, photoelectric retro-reflective sensors, ultrasonic sensors and others.
