CERTH explains their work in AI-enabled Cell-level perception methods.
The objective of this task is to provide the envisaged robot with the ability to perceive the industrial environment so as to effectively collaborate with the humans and assist them with the Waste Electrical and Electronic (WEEE) device recycling process. Towards this direction, CERTH recorded multiple recycling procedures of four WEEE devices, namely PC Towers, Microwave Ovens, Flat Panel Displays and Emergency Lamps, focusing on the human-robot collaboration aspect during the WEEE device disassembly, thus creating a WEEE device component detection dataset by manually annotating a number of the recorded frames.
Given this dataset, CERTH deployed state of the art Computer Vision methods utilizing Deep Learning techniques in order to train a Convolutional Neural Network (CNN) capable of detecting the four WEEE devices as well as their respective components during the various stages of the disassembly process. Additionally, CERTH enhanced these methods by proposing an anchoring mechanism targeting specifically the detection of small objects.
