Based on deep learning, the SICK application system detects whether a sorting tray in a logistics hub is actually loaded with only one object. (Picture: SICK)

SICK's sensor intelligence profits from deep learning

SICK presents new software applications based on deep learning algorithms

Starting now, users of system solutions in logistics automation can profit from the benefits of the new technology. Using deep learning, sensors perform intelligent in the automated detection, testing and classification of objects or features, which were reserved for humans so far.

Deep learning, a sub-area of machine learning, ranks among the probably most significant future technologies in the field of artificial intelligence, and is a long-term driver of Industry 4.0 at the same time.

After SICK reported the successful application of deep learning algorithms in the first pilot programs in January 2019, the company announced a new software application for system business in logistics automation based on deep learning at the Hannover Messe.

In this application, the deep learning system detects whether a sorting tray in a logistics hub is actually loaded with only one object. This makes the stream of goods more efficient.

Training for the sensor

Neuronal networks are used to realize deep learning. Compared to the classical process for developing algorithms, which is mainly characterized by manual development of a suitable feature representation, a neuronal network is trained to optimal features for its task and can be retrained again and again with suitable data in order to adapt to new circumstances.

SICK is using a powerful, independent in-house computer and IT base as the executing unit both for development of the training data set by collecting and assessing thousands of images and examples as well as for training the neuronal networks.

The extensive computation of the complex operations of the deep learning solution for training is done on computers with high GPU performance specially equipped for this purpose. The new deep learning algorithms generated in this way are provided locally on the sensor, making them fail-safe and directly available, for example on an intelligent camera.

Development of the deep learning sensor portfolio

With the implementation of deep learning in selected sensors and sensor systems, SICK is igniting the next level in AppSpace after the SICK AppSpace eco-system–a new sensor software concept which creates adaptable and future-proof solutions for automation applications.

Other image-processing sensors and cameras are naturally also included in the coming products, which work with the new technology and whose customer-specific adaptation generates real added value for the user.

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Oliver Huther
Oliver Huther
Business Development Manager RFID
Düsseldorf, Germany
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