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Discussion of using Machine Learning for Safety Purposes in Human Detection

: Bexten, S.; Saenz, J.; Walter, C.; Scholle, J.-B.; Elkmann, N.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020. Proceedings : Vienna, Austria - Hybrid, 08 - 11 September 2020
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-8956-7
ISBN: 978-1-7281-8957-4
International Conference on Emerging Technologies and Factory Automation (ETFA) <25, 2020, Online>
Fraunhofer IFF ()

The reliability and robustness of systems using machine learning to detect humans is of high importance for the safety of workers in a shared workspace. Developments such as deep learning are advancing rapidly, supporting the field of robotics through increased perception capabilities. An early detection of humans will support robot behavior to reduce downtime or system stoppages due to unsafe proximity between humans and robots. In this work, we present an industry-oriented experimental setup, in which humans and robots share the same workplace. We have created our own dataset to detect humans wearing different clothing. We evaluate Faster R-CNN and SSD which are state-of-the-art detectors on two different camera viewpoints. In addition, this paper elaborates on the requirements for validating the safety of such a system to be used in industrial safety applications.