Simge Nur AslanAyşegül UçarCüneyt GüzelişGuzelis, CuneytUcar, AysegulAslan, Simge NurM. Ivanovic , T. Yildirim , G. Trajcevski , C. Badica , L. Bellatreche , I. Kotenko , A. Badica , B. Erkmen , M. Savic2025-10-062020978172816799210.1109/INISTA49547.2020.91946442-s2.0-85092000258https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092000258&doi=10.1109%2FINISTA49547.2020.9194644&partnerID=40&md5=ee87d3ae22facc17806812dd42b4b165https://gcris.yasar.edu.tr/handle/123456789/9169https://doi.org/10.1109/INISTA49547.2020.9194644In 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 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessConvolutional Neural Networks, Humanoid Robots, Object Recognition, Anthropomorphic Robots, Complex Networks, Convolutional Neural Networks, Deep Neural Networks, Intelligent Systems, Learning Systems, Object Recognition, Social Robots, Humanoid Robot, Layer Structures, Learning Methods, Learning Models, Model Complexity, Neural Networks Model, Security Environments, Specific Tasks, Deep LearningAnthropomorphic robots, Complex networks, Convolutional neural networks, Deep neural networks, Intelligent systems, Learning systems, Object recognition, Social robots, Humanoid robot, Layer structures, Learning methods, Learning models, Model complexity, Neural networks model, Security environments, Specific tasks, Deep learningObject RecognitionConvolutional Neural NetworksHumanoid RobotsConference Object