Fast Object Recognition for Humanoid Robots by Using Deep Learning Models with Small Structure - Aslan, Simge Nur; Ucar, Aysegul; Guzelis, Cuneyt (Institute of Electrical and Electronics Engineers Inc., 2020)
In these days, the humanoid robots are expected to help people in healthcare, house and hotels, industry, military and the other security environments by performing specific tasks or to replace with people in dangerous scenarios. For this purpose, the humanoid robots should be able to recognize objects and then to do the desired tasks. In this study, it is aimed for Robotis-Op3 humanoid robot to recognize the different shaped objects with deep learning methods. First of all, new models with small structure of Convolutional Neural Networks (CNNs) were proposed. Then, the popular deep neural networks models such as VGG16 and Residual Network (ResNet) that is good at object recognition were used for comparing at recognizing the objects. The results were compared in terms of training time, performance, and model complexity. Simulation results show that new models with small layer structure produced higher performance than complex models. © 2020 IEEE.
2020 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2020 -- 24 August 2020 through 26 August 2020 -- Novi Sad -- 163005
Keywords: convolutional neural networks; Humanoid robots; object recognition